Abstract

We present an approach to understanding how to create a consulting business for a personal service, in this case hair beauty. The approach uses experimentation, in the form of systematically varied ideas (Mind Genomics.) The strategyis to expose respondents to combinations of services, identify which particular ideas in the combination ‘drive’ positive reactions, and then focus on those ideas in communication. Rather than asking respondents, Mind Genomics works with combinations, presented rapidly, forcing the response to be intuitive, rather than considered. Mind Genomics reveals new-to-the-world groups of consumers, mind-sets, who respond to different messages in communications, and identifies individuals with these mind-sets through a PVI, personal viewpoint identifier.

Introduction

The business of beauty, ‘hope in a bottle’ as some have called it, continues to grow. The desire to be beautiful to others, seemingly built-in to our condition as human beings, continues to drive business growth as the economies of the world improve, these economies moving into the 21^st century, and expanding beyond subsistence to better living, and even to living at the ‘high end,’. The rise of wealthy multi-national companies, specializing in the creation of personal ‘beauty’ in all forms, for all parts of the body, attests to ineradicable desire of people to look attractive.

Macro-economic studies of the growth of the beauty industry can go just so far, and no further. The expertise of marketing and market researchers, replete with their knowledge about the industry, the solution providers (e.g., salons, products) and the customers, provide a lot of information and indeed with the Internet a torrential, ever-increasing amount each day. Whether one reads the newspapers, listens in on social media, or works in salons and stores, one cannot escape the world of beauty, massive, dynamic, growing. The industry reports, the stock market, the newspaper and other sources of ads and promotions attest to the dynamism.

What then about the individual, however? We mean here the consumer who buys the beauty product or service. What can we learn about them, information beyond the conventional information of ‘who they are’, and ‘why they buy?’ We don’t mean the standard information available from trend studies, from so-called Big Data, or even from focus groups convened to learn how to sell a product or service. Rather, we mean here the mind of the individual, when dealing with a product in the world of beauty.

Sadly, in the world of science there is relatively little research devoted to the way people make ordinary decisions. There are, of course, studies of entire categories and verticals, but these studies tend to be cross-sectional, in the spirit of a macro-economic analysis, such as what are people in general thinking, what are people, in general, buying, and so forth. The science which emerges from these studies tends to be strongly driven by theory, by mathematical models, and replete with generalities about human behavior gleaned from the analysis. In contrast, there is very little science of ‘every day’ experience. We know that people experience daily life, and make decisions, one decision after another. But what can we learn about the structure of these decisions? Can we create a science of daily life, almost a science of the mind as the mind or the person confronts the very ordinary, quotidian situations, which make up day to day living?

There are, of course, academic studies, although far fewer than one might guess, especially in the world of beauty. Studies of beauty as they pertain to daily life tend not to be the topic of science, although when one searches hard enough, there are many papers, most about beauty in the culture rather than beauty and specifically hair as a topic of science, from the person’s point of view [1–5]. There is, of course, a literature on beauty from the point of view of science, although this information tends to be clinical, even though it deals with an emotionally important topic [6,7] The real and often riveting information about one’s experience with beauty, decision-making, and actions comes from the popular press, from news articles, and stories to interest lay readers, who find utterly fascinating these stories about beauty and its many facets [8–12].

Mind Genomics as an organizing principle

In the world of products, services, and marketing, professionals are realizing that it is increasingly impossible to make judgments about business tactics without the necessary evidence. In previous decades the beauty business as well as the perfume business were dominated by peoples who we would call ‘business titans’ when running a large corporation, or superb professionals when designing products, especially perfumes. The cosmetic industry was spared some of the cult of personality because it had to deal with product functionality as well as product image. Nonetheless, the cult of personality left a legacy of relatively little knowledge about the mind of the customer. Compared to the world of food, the world of cosmetics and beauty is lacking in depth knowledge of customers, and is still heir to some of the forces of charismatic personalities.

Author Moskowitz has developed a new approach to understanding the consumer, not so much based on conventional research such as focus groups, surveys, or tracking studies, as based on the world-view of experimental psychology. The approach is morphing into an emerging science called Mind Genomics, which is executed as a survey but in fact is an experiment to probe the mind of the customer[13–16].

A good analogy for Mind Genomics, elaborated below, is ‘the MRI of the mind.’ The intellectual history of Mind Genomics can be traced to the pioneering work of psychologists and statisticians [17], as expanded by Green and his associates at the Wharton School of Business, The University of Pennsylvania [18,19].

The fundamentals of Mind Genomics are simple, elaborated in the four steps below:

1. EXPERIMENT: Approach the topic as an experiment, present test ideas (message) in combinations (vignettes), acquire ratings, and deconstruct the ratings to the contribution of the individual ideas. The statistics involved are subsumed under the rubric of experimental design [20].
2. MIND-SETS: Identify different mind-sets, defined as arrays of ideas which focus on different aspects of the topic. The statistics involved are subsumed under the rubric of clustering, which places people or other objects into non-overlapping groups, based upon the pattern of features [21,22].
3. ASSIGNMENT OF NEW PEOPLE TO MIND-SETS: Assign new people to a specific mind-set, based upon a short test. The approach is an algorithm developed by author Gere, and called the PVI, the personal viewpoint identifier
4. SEND THE ‘RIGHT MESSAGE’ TO THE ‘RIGHT PERSON’ AT THE ‘RIGHT TIME.’ Present each person with the appropriate messages, defined as those messages which appeal to the mind-set [23].

Doing the Mind Genomics study

During the past 15 years the Mind Genomics protocol for research has become increasingly standardized in terms of the research choreography. The standardization enables the researcher to set up the study quickly, in a matter of hours, executed the study, and have results back in a matter of three-four hours, with the data analyzed. The rapid design, implementation, and analysis, has occurred because the Mind Genomics process has been ‘templated’. We present the research template here, a template that has been followed for many dozens of studies.

1. Define the topic. For this study the topic is ‘what is important in one’s choice of a beauty hair consultant from the point of view of an ordinary individual?’ For the best results, the scope of the topic should be limited to a specific and well-defined topic, a topic which can be expressed in a single sentence. Most researchers need practice in order to define the topic in a succinct, operationally meaningful way, a way whose description can produce a word picture in the mind of an individual not familiar with the topic.
2. Define a set of questions which tell a story. These questions (or silos) are never shown directly to the respondents in the Mind Genomics study. Rather, the questions are used to elicit answers (elements), these answers in turn shown to respondents in various combinations, as described below. It is worth noting that the most difficult part of the Mind Genomics study comes in this second step. Many researchers have a very hard time thinking in this structured, story-telling fashion. The discipline required to ask the series of related questions comes with practice, and in some ways the Mind Genomics process ‘re-wires’ the mind of the respondent. Table 1 presents the four questions, and the four answers for each question.
3. Combine these answers into short, easy to read combinations, so that the respondent can quickly read and evaluate. Figure 1 (left panel) shows an example of a vignette as the respondent will see it, with the view being the smartphone. The same vignette can be configured for a tablet or a personal computer, as shown in Figure 1 (right panel.)

Table 1. The raw material for the Mind Genomics study, comprising four questions which ‘tell a story’ and four answers to each question. HBC = Hair Beauty Consultant

Figure 1. Examples of a vignette, as it appears on the screen of a smartphone (left), and the same vignette as it appears on the screen of a tablet or personal computer (right)

The vignette shown in Figure 1 contains no connectives. Rather, the elements are placed on the page, left-justified, one element following another, the elements on separate lins Often, those who will use the research findings feel that it is impossible for the consumer respondent to rate the combination because the elements seem to have been thrown together haphazardly. Most of the experience of researchers working in the evaluation of combinations of ideas has been focused on getting the stimulus, the vignette, ‘just right,’ connectives and all, with the vignette appearing as a paragraph. That paragraph format, so rational and acceptable to many, becomes, in fact, quite onerous to read after the respondent has read and rated 3–4 of these paragraphs.

Mind Genomics works within a different world view, focusing on presenting messages as they are presented in the real world, unconnected, almost ‘thrown’ at the respondent. It is the job of the respondent to make a judgment as in real life. The structure is difficult to discern, so that in the end, most of the respondents simply ‘give up,’ and assign ratings according to their intuition, System 1 in the words of Nobel Laureate Daniel Kahneman [24].

Despite the apparent randomness of the combinations, nothing could be further from the truth. The reality is that the vignettes, the test combinations, are crafted through an underlying experimental design which prescribes the precise set of 24 combinations to make, so that each element appears equally often, all 16 elements appear in a statistically independent fashion, each vignette comprises 2–4 elements and at most one answer from each question (i.e, at most one element from each silo). A permutation scheme ensures that each respondent evaluates different combinations. That is, the combinations tested by one respondent are different from the combinations tested by any other respondent. The permutation scheme is discussed by Gofman&Moskowitz [25], based upon a patent [26].

Table 2 presents data from the first eight vignettes from a respondent, along with the preparation of the design and data for analysis by OLS (ordinary least-squares) regression. The respondent’s ID number is 7. The Mind Genomics system does not record WHO the respondent IS, but records the date of birth and the gender. Thus, it is possible to use age and gender as stratifying variables. The respondent in this study was also asked about the concern with their hair. Two of the four responses were either not concerned or only mildly concerned with their hair. Respondents choosing one of these two answers were put into the group stating that there was little or no concern. The remaining respondents chose answers reflecting modest or strong concern, and were put into the second group, who are concerned with their hair.

Table 2. Experimental design underlying the vignettes

The basic information we have about the respondent is that she is a 65-year-old female who states that she is concerned with her hair. Furthermore, as we will see later in the paper, the respondent falls into Mind-Set #3, based upon the pattern of her responses. The assignment of respondents to one of a set of complementary, mutually-exclusive and exhaustive mind-sets for this particular topic of hair care consulting provides yet a fourth way to define WHO the respondent is, this time based upon how the respondent thinks about hair beauty consultants.

Below the respondent specifications are listed the identification code for the test elements which appeared in vignettes 1–8, respectively. Each vignette has at most one element from each silo, or one answer from each question, but in reality there are vignettes entirely lackingan answer to one question (e.g., vignette 1 lacks an answer to Question B), and vignettes entirely lacking an answer to two questions (e.g., vignette 6 lacks an answer to both Question A and Question C, respectively.) Respondents have no problem evaluating vignettes which are incomplete, since respondents ‘graze’ for information, rather than slavishly read the vignette word by word.

As the experimental design is laid out, most computer programs have a difficult time analyzing the data. The experimental design is not intrinsically numeric, but rather descriptive. It is important to transform the data to a form that the statistics program can use. One very straightforward way to prepare the data for analysis recodes the experimental design to 0’s (when an element is absent from a vignette), or 1’s (when an element is present.)

Table 2 further shows the recoding of the design from four rows to 16 rows. Each row corresponds to one of the 16 elements. There are 16 rows, labelled A1 to D4, to represent each of the 16 answers or elements in a vignette. Each column, in turn, corresponds to one of the eight vignettes. The cells show the coding of a specific vignette and a specific element. When the cell has a ‘1’, the vignette contains that element. When the cell has a ‘0,’ the vignette lacks that element. Looking down at the composition of one vignette, we see at most four ‘1’s and the rest ‘0’s, which tells the computer program and the researcher that the vignette has no more than four elements, and tells the program which specific four (or three or two) elements are present in the vignette.

Below the binary recording are the response data, comprising the actual rating (1–9), the binary transform for positive responses (7–9 → 100, 1–6 → 0), the binary transform for negative responses (1–3 → 100; 4–9 → 0), and the response time in sections. The binary transform is used in the spirit of consumer research, which continues to present data to the end-user as binary, NO vs YES. The specific division of the 9-point scale into the two asymmetric halves, 1–6 versus 7–9 was done following the standard research protocol used in Mind Genomics studies since the late 1980’s, 30+ years ago.

The arrangement of the data in the form shown in Table 2 allows the computer program to process the data in a numeric form, creating a ‘model’ or equation. The model or equation shows how the presence/absence of the elements in a vignette ‘drive’ the response. The creation of these models, the interpretation of their meaning, and the application of the results to practical issues will be the topic of the rest of this paper.

Results

How do individual respondents rate the vignettes?

Each respondent rated 24 different vignettes. We have two transformation or recodings of the same data, a positive recoding for liking, and a negative recoding for disliking. The transformation of the vignettes tells us whether, for the particular vignette the respondent ‘likes’ the vignette (positive recoding: ratings 7–9 → 100), whether the respondent dislikes the vignette (negative recoding: ratings 1–3 → 100) or whether respondent is indifferent (neither like nor dislike).

The average transformed rating for each respondent shows the proportions of positive versus negative average responses. A respondent who liked every one of the 24 vignettes would have a value of 100 across the 24 vignettes for the transformation of ‘like’. That respondent would have all 0’s for the recoding for dislike. Thus the average of the positive recodes for an individual tells us the degree to which the individual ‘likes’ everything. The average of the negative recodes for the same individual tells us the degree to which the individual ‘dislike’ everything.

When we plot the average likes (abscissa, X axis) versus the average dislikes (ordinate, Y axis), with one point for each respondent,
Figure 2 shows us that most of the respondents cluster either at the bottom of the graph (like most of the vignettes, dislike none or a few), or cluster at the left side of the graph (*dislike most of the vignettes, like none or a few). Respondents are polarized. They either like or dislike what they read. There are only a few respondents who show indifferent responses. These would be in the middle of the graph.

Figure 2. Scatterplot, showing the distribution of positive and negative averages regarding the rating of the 24 vignettes, after the binary transform. Each letter corresponds to a respondent. Y = respondent says concerned with hair; N = respondent says not concerned with hair.

When we classify the respondents by their self-stated concern with hair, we can represent them by N (not interested) or Y (interested). Figure 2 suggests that those who say that they are concerned with their hair tend to be more positive, on average, and those who say that they are not concerned with their hair tend to be more negative, both with respect to rating the vignettes.

Creating a model by OLS, ordinary least-square regression

The essence of Mind Genomics is to understand the specific ‘drivers’ of responses, which in our case becomes the specific messages driving a respondent to say: ‘I am interested in a beauty consultant.’ The rating scale conveys that interest, doing so for the different vignettes that were created. The notion of exposing respondents to different combinations comes from the world of human experience, where the most typical situation confronting a person is a set of features or items in an environment, and the reaction of the person to that combination. It is often impossible for a person to identify the particular features of the combination confronting the person responsible for the subsequent action taken by the person.

When the researcher combines the different elements or messages into a combination using experimental, the above-mentioned vignette, the issue identifying the ‘driving’ element is made simpler. Various statistical techniques falling into the general statistical system called ‘regression’ relate the independent variables, those features driving the response, to the dependent variable, the nature of the response itself. There is simply a need to ensure that the predictor variables of interest are ‘statistically independent,’ and not strongly linked with each other. Regression disentangles the response to the mixture into the contributions of the components of the mixtures, in our cases the messages

We use OLS (ordinary least-squares) regression to relate the presence/absence of the 16 elements to the rating, in our case defined as the 0/100 after binary transformation. Table 2 showed us the way the data are formatted. We know the combination, and we measure the response. For then regression analysis whose results are shown in Table 3, we combined the data from all respondents who are members of the class, ‘class’ or ‘group’ defined as total, as gender, as age, or as self-defined concern with one’s hair.

Table 3. Performance of the elements by total panel and key self-defined subgroups

Table 3 shows the key information emerging from the regression analyses. We interpret the data in the following way:

1. The additive constant. This value is the estimated percent of responses to the vignette that would be 100 (viz., originally 4–5) in the absence of elements. The reader will at once realize that all vignettes comprised as many as four elements, as few as two elements, and never one or no elements, respectively. Thus, the additive constant is a baseline value, purely an estimated parameter.
2. The additive constant can be interpreted as a baseline of acceptance, when we look at the binary transformed data. It is the estimated percent of responses that would be 4–5 on a 5-point scale in the absence of elements. When our goal is to achieve a high total score, beginning with a high additive constant means that the basic feeling towards the product or the service is strong, and the element do not have to do much work. With a low additive constant, the opposite is the case, and the elements must do ‘all the work.’ The additive constant need not be a positive number. The mathematics behind the additive constant and the individual element-linked coefficients, regression analysis, does not set limits on the additive constant
3. The additive constants range dramatically, from a high of 50 for respondents ages 50+ (older respondents are basically interested in a hair consultant), to a low of -19, virtually 0 for respondents ages 18–29 (younger respondents are basically uninterested in a hair consultant.)
4. Surprisingly, those who say that they are concerned about their hair are less interested in the hair beauty consultant (additive constant = 17), versus those who say that they not concerned about their hair (additive constant = 39).
5. The data suggest that it will be the elements which make the difference.
6. To make reading the data easier, we have shaded all elements which achieve a coefficient of 8 or higher in any subgroup. Studies using Mind Genomics suggest that the statistical value significance is around 8 or so, using the principles of inferential statistics. Observations by author Moskowitz using these data in many studies further suggest that when the coefficient is 8 or higher, the element performs well in other applications, such as advertising.
7. The total panel shows no elements which drive interest. Although this finding may be disconcerting to many, since research is presumed to show opportunities, the reality is that most of the messages studied by Mind Genomics simply do not persuade, do not drive people to try the product, use the service. Even though a message may have been used for years does not make the message by definition ‘sacred,’ and an accepted part of one’s marketing and sales portfolio.
8. Dividing respondents by ender shows similar additive constants, but only one strong element, that element appealing to males: B2 – hair consultant writes for social media
9. Dividing respondents by age shows an exceptional number of messages appealing to the younger respondent, age 18–29

present alternative best 2 or 3 solutions
thorough discussion after examination
hair consultant writes for social media
present products and/or treatments for the client
present products for the clients
client has long term relationship … personal project
weekly meetings on computer to SEE and DISCUSS progress
beauty salons often recommend
client has project and monthly visits
client gets reduced salon prices as part of treatment

The older respondents (age 50+) show only two very strong elements
works with overly oily hair
hair consultant gives courses for new hair professionals

Those who say that they are not concerned with their hair
hair consultant writes for social media
hair consultant gives courses for new hair professionals

Difference mind-sets searching for hair care

The premise of Mind Genomics is that in any topic area where human decisions are made on the basis of exterior information, there exist different groups of ideas which ‘travel together.’ These ideas are called mind-sets. They are embodied in an individual who is said to ‘hold’ a specific mind-set, but they are not the individual. They are cohesive sets of ideas. We only discover these ideas, however, through experimentation. We need people to help us reveal these mind-sets.

The mind-sets are discovered by a simple statistical method called ‘clustering,’ which puts together different things (e.g. ideas) based upon the similarity of their patterns. Clustering does not necessarily reveal fundamental, basic ideas, although it may. Rather, clustering is a heuristic, designed to create smaller, non-overlapping groups from a large, perhaps inchoate group of items a group without seeming commonalities.

Clustering comes in many variations. With each variation of the clustering method emerges a different set of clusters, or in our case, mind-sets. The fact that there is not a perfect set of fundamental groups should not be a cause for upset. All clustering attempts to do is to find approximately different groups, so that these groups can be treated in a more appropriate way for themselves. Rather than assuming all people to be identical in what they want, or to assume that each person is totally unique, making personalization almost unachievable, clustering finds approximations to different grounds, which can be then studied separately to see the messages to which they respond.

Mind-set segmentation in Mind Genomics enjoys the benefit of segmenting a population on the basis of the precise words that will be used to send them offers. That is, instead of segmenting or clustering the population on the basis of some factors which clearly divide them, and then looking for the messages appropriate for each segment, Mind Genomics segments the people on the messages that are relevant to the topic. The segmentation is more crystallized by Mind Genomics because the segments are created on precisely the topic which is being explored.

The mechanics of clustering for these data follow the now-standard process for Mind Genomics studies. We begin by computing a ‘distance’ between every pair of respondents, that distance computed by a simple formula: D = (1 – Pearson Correlation). The Pearson Correlation coefficient tells us the degree of linear relation between two variables. When two variables are perfectly related to each other in a positive sense, the Pearson Correlation is +1 so the variable D becomes 0. This makes intuitive sense. The two variables behave identically. When two variables are perfectly correlated, but moving in opposite directions, the Pearson Correlation is –1, so the variable D becomes 2.

The clustering algorithm works with these distances, to put the different respondents into either two or three non-overlapping clusters or mind-sets. The process attempts to make the set of distances D values, have as great a value for the distances between clusters or mind-sets, and at the same time have a small value for the distances between members within a cluster.

Table 4 shows the coefficients for the total panel, for the two complementary Mind-Sets when we extract two clusters, and the three complementary Mind-Sets when we extract three clusters. We do not know which group of Mind-Sets to choose. Thus far, the process has been strictly mathematical, working with the values of the abovementioned variable ‘D’ or distance between upon the Pearson Correlation.

Table 4. Performance of the elements by total panel, two emergent mind-sets and three emergent mind-sets.

We employ two criteria to add judgment to the process:

1. Parsimony. When clustering, the better solution should be the smaller solution, but still one that can be interpreted immediately, because it makes sense. In our study we have extracted both two and three Mind-Sets. We do not know which of these two we will select. Both are parsimonious.
2. Interpretability. The clusters or Mind-Sets should ‘tell a story,’ and an obvious one. When we look at Table 4, we see that there are few strong performing elements when we extract two Mind-Sets by clustering. In contrast, when we extract three Mind-Sets, we find three interpretable groups:

Mind-Set C1 – Not really interested in anything, not a prospect for a hair beauty consultant

Mind-Set C2 – Want a hair consultant who is clearly an expert, and ‘knows’ people and products

Mind-Set C3 – Want a hair beauty consult who is involved in a long-term relation with client

Finding these respondents in the population

How one finds these Mind-Sets in the population has challenged researchers for a number of years, ever since the issue of applying the data to commercial and social uses has arisen. Decades ago, the market researcher William Wells introduced the idea of psychographic segmentation [23], suggesting that people could be divided by their minds and values. This led to lifestyle segmentation, based upon the way a person lives, and afterwards to behavioral segmentation, especially when shopping on the web. All of these segmentations work, dividing the people, but not identifying what to say for the specific situations of one’s life, the daily micro-worlds in which we live. Mind Genomics does so, but faces the same problem as all other segmentations based upon how people think.

An analysis of who the respondents are by age, gender, and interest in caring for one’s hair suggests that these Mind-Sets are spread through the population in a way that cannot be predicted easily from knowing WHO the respondent is, or CONCERN that the respondent has about her or his hair. Thus, we are left with a powerful finding about the mind of the prospective client for hair beauty consulting, yet the frustration of knowing that although these prospects exist, they cannot easily be identified. In fact, they not even know that they are prospective clients.

Author Gere has developed the PVI, the personal viewpoint identifier, which allows a respondent to answer six questions based upon the 16 answers shown in Table 1. Figure 3 shows the six questions, and the two possible answers from each question. The pattern of answers from a single is used in conjunction with the table of coefficients (Table 4). There are 64 possible patterns of responses, when the question has two possible answers. The 64 patterns are mapped to membership in one of the three Mind-Sets. Once the person has completed the PVI, in 30 seconds or faster, the person’s mind-set can be discovered, least with less guessing than before. Figure 4 shows the feedback for each mind-set. This feedback can either be given to the respondent and/or stored by the researcher/consultant for future efforts with this particular individual. The actual link to the PVI for this study as of this writing (June, 2019) can be found at: http: //162.243.165.37: 3838/TT33/

Figure 3. The PVI for the Hair Beauty Consultant

Figure 4. The feedback from the VPI. Each mind-set has its own feedback, sent either to the beauty consultant and/or the client.

Beyond what interests to what engages – Response time

The foregoing sections reveal the dramatic differences among respondents in the degree to which specific messaging appeals to them. Another dimension of important is engagement, the degree to which a specific message engages attention by being read.

The Mind Genomics system measures the response time for each vignette, doing so to the nearest tenth of a second. Since the experiment is conducted on the Web, without any supervision, occasionally (about 10% of the time) the response time is exceedingly long, last 10 seconds or longer, a time that other studies have shown to be exceptionally long. For all response times exceeding 9.0 seconds, we truncated the response time to 9.0 seconds.

Figure 5 shows the average response times across the 24 vignettes for each respondent. The respondent either one who defines herself/himself as concerned about hair (Y) or not concerned about hair (N). Our ingoing hypothesis was that those respondents who say that they are concerned about hair would spend, on average a longer time reading the vignette. We reject the hypothesis. The response times are similarly distributed, so any difference in average across all respondents would be minor at best.

Figure 5. Average response times across the 24 vignettes. Each letter corresponds to a respondent, who self-defines as either concerned about their hair (Y) or not concerned about their hair (N).

The degree to which the individual elements can engage may also be estimated using regression analysis. The ingoing experimental design is known for each respondent, as is the response time in seconds. We can create a simple model relating the presence/absence of the 16 elements to the response time. The model is written without an additive constant, under the assumption that in the absence of elements the response time would be 0 seconds. The equation is expressed asResponse Time = k1(A1) + k2(A2) … K16(D4)

Table 5 shows the coefficients for the response time models. The models were estimated from all the data relevant for the key subgroup. That is, the only data used to estimate the model for males are the data from males. Similarly, the only data used to estimate the model for Mind-Set 3E are respondents in Mind-Set 3E.

Table 5. Engagement – The estimated response times attributed to each message or element.

Table 5 suggests some two simple rules of thumb:

Rule 1 – To engage (although not necessarily to persuade) talk about the process, painting a word-picture of what the client gets as an individual

present alternative best 2 or 3 solutions
client gets reduced salon prices as part of treatment
hair consultant gives courses for new hair professionals

Rule 2 – To not engage, be general, and talk about the problem being solved

gives real professional advice
often works with hair which is falling out
works with overly oily hair

Discussion

The results of this study give a sense of the complexities of daily life. Rather than attempting to introduce a new ‘theory’ of consumer behavior (top down thinking) using a mundane issue such as choosing a hair beauty consultant to confirm or falsify the tenets of such a ‘theory,’ Mind Genomics moves in an orthogonal direction, to ‘map’ the mind. There is no theory, for which the topic of beauty consultant can affirm or falsify. Rather, there is the important effort to be a ‘cartographer’ of the mind, to understand the nature of what confronts people in the every-day, and then construct a science of this ‘ordinariness.’ As these data suggest, the ‘ordinary’ is quite far from simple. There are different mind-sets to be uncovered, different messages which engage versus which are skipped over, and so forth. Indeed, the every-day is far from mundane, but rather presents an entirely new world for science to explore, a world where the discipline of science can fruitfully inform the daily rhythm of life.

Three directions using these results

Our efforts to understand the mind of the consumer when choosing a hair beauty consultant move us in three different directions.

1. The decision criteria of everyday. The study revealed two major segments with diverse interests. One is interested in the topic, in the expertise, and probably in the facts. The other is interested in a personal relation. We might move beyond the specifics of hair care consulting, and ask whether this type of division, expertise-respecting vs relationship-seeking, characterize other types of subjects, beyond hair care. Could the experiment with Mind Genomics have uncovered a general division of the mind? And, following the proposition that there are these two main mind-sets, does a person ALWAYS fall into one mind-set or the other, or is the membership labile, a function of the topic, and who the person IS at the moment of the study.
2. Applying science for practical benefit. In many disciplines, the mere thought that the data could be used for practical decision-making means that the data are not appropriate for science. Mind Genomics in general, and the results from this study in particular, enable the user to conduct business and daily life in a more efficient manner. Knowing what specific messages to give to a person based upon the person’s mind-set, AND having a way to assign a person to a mind-set, are extremely important for today’s world, for commerce. Increasingly, people are feeling that they don’t want commercial organizations to ‘track their behavior,’ because they feel that such tracking violates the ingredient. Indeed, recent developments in privacy have led to the adoption of a major privacy initiative [27], designed to avoid gathering and using too much data about a person. Fortunately, the only information one needs of a private nature comes from the momentary interaction of a person (identity masked) and the attitudinal questions from the PVI. That data need not even be stored, but simply used at the instant of transaction in order to give a sense of the mind of the prospect to a company
3. Creating a data warehouse for knowledge of beauty. The metaphor of Mind Genomics is that for each aspect of experience there are different ways to respond to that aspects, different features about the aspect, and of course, different messages. The objective of a Mind Genomics study is to ‘map’ these ways, to reveal the science of every-day. In recent years author Moskowitz and colleagues have suggested that another opportunity may be to create large-scale databases, of many studies within the same topic, here beauty. The studies are straightforward to design and to execute. The world of beauty itself may comprise dozens of different topics, each of which generates its own Mind Genomics study, and in turn each Mind Genomics study uncovers the mind-sets, and is finished off by the PVI, personal viewpoint identifier, for that topic. What might be the arc of knowledge if instead of one PVI completed by a person, 20 or more PVI’s were to be completed, for the wide arc of beauty. Each person would, in fact, generate a vector of some 20 different mind-sets to which a person might belong, based on the patterns of the individual’s separate PVI’s. Such a vector of PVI’s could form the basis of a deep understanding of the mind of people in a life-relevant area (beauty), with the membership patterns of hundreds of thousands of individuals established through a set of PVI’s correlated with biological factors, social factors, and one’s own intellectual/personality factors. Such is the promise of a Mind Genomics, the science of the everyday, with a simple demonstration here in this paper for a simple, but relevant topic to daily life, one’s hair.

Acknowledgment

Attila Gere thanks the support of the Premium Postdoctoral Researcher Program of the Hungarian Academy of Sciences.

References

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Abstract

Background: Intrauterine growth restriction (IUGR) is the most common risk factor associated with perinatal mortality after excluding congenital anomalies 1. IUGR refers to a fetus that has failed to achieve its genetically determined growth potential and affects up to 7–10% of pregnancies 2. Fetal growth restriction is associated with an increase in perinatal mortality and morbidity. This is because of a high incidence of intrauterine fetal demise, intrapartum fetal morbidity, and operative deliveries. Neonates affected by IUGR suffer from respiratory difficulties, polycythemia, hypoglycemia, intraventricular hemorrhage, and hypothermia 3,4,5.

Objective: 1. Primary objectives: to evaluate the results of Microarray in symmetrical IUGR babies. 2.Secondary objectives: to compare between microarray positive babies and negative babies regarding: gestation age, weight, Apgar score, need and indications for NICU admission as well as length of NICU of stay.

Result: Between Jan 2016 and December 2017 total 10,695 babis were born. Among that 578 babis were IUGR (501 asymmetric and 77 symmetric IUGR). Total 71 babies were taken in our analysis after excluding 3 down syndrome and 3 babies part of multiple pregnancy. Microarray test had positive findings in 14/71 (19.7%). There were copy number changes of unknown significance in 8/71 (11.2%)

Conclusion: Most of the microarray test results were copy number changes of unknown significane which is comparitvely much higher than reported prevalence. Microarray positive IUGR had comparable NICU admissions to negative result group but their duration of stay, initial lower apgar scores and thrombocytopenia was significantly higher. This may be because, even copy number changes has unknown significane, they may have some clinical effect which is not known till now and may need further studies and long term follow up for those cases.

Keywords

Intrauterine growth restriction (IUGR), Chromosomal microarray analysis (CMA), Neonatal intensive care unit (NICU)

Abbreviations and Acronyms: Intrauterine growth restriction (IUGR), Chromosomal microarray analysis (CMA), Copy-number variants (CNVs), Toxoplasmosis, rubella, cytomegalovirus, rubella (TORCH), Neonatal intensive care unit (NICU), variant of uncertain significance (VUS).

Introduction

Intrauterine growth restriction (IUGR) is the most common risk factor associated with perinatal mortality after excluding congenital anomalies [1]. IUGR refers to a fetus that has failed to achieve its genetically determined growth potential and affects up to 7–10% of pregnancies [2]. Fetal growth restriction is associated with an increase in perinatal mortality and morbidity. This is because of a high incidence of intrauterine fetal demise, intrapartum fetal morbidity, and operative deliveries. Neonates affected by IUGR suffer from respiratory difficulties, polycythemia, hypoglycemia, intraventricular hemorrhage, and hypothermia [3–5].

Intrauterine growth restriction (IUGR) can be either symmetric or asymmetric. Symmetric IUGR is characterized by a similar and proportionate reduction in all auxological parameters, including weight, length, and cranial and abdominal circumference. Early gestational growth retardation would be expected to affect the fetus in a symmetric manner, and thus may have permanent neurologic consequences for the infant. Causes include chromosomal disorders, including trisomy 13, 21 and 18 and some rare genetic syndromes, such as Cornelia de Lange and Silver Russell syndromes and early congenital infections (rubella, cytomegalovirus, rubella, toxoplasmosis) [6]. Early-onset forms of IUGR represent more severe conditions and more links with perinatal mortalities [7].

Asymmetric IUGR is characterized by a greater reduction in body weight, when compared to the length and more commonly due to extrinsic influences that affect the fetus later in gestation, such as preeclampsia, chronic hypertension, and uterine anomalies. Karyotyping is an important investigation for symmetric IURG for chromosomal disorders particularly for babies with abnormal features. Chromosomal microarray analysis (CMA) is a high resolution, whole-genome screening technique that can identify most of the chromosomal imbalances as well as smaller submicroscopic deletions and duplications that are referred to as copy-number variants (CNVs) and has quicker turnaround time than a karyotype [8–10]. As microarray has a higher diagnostic yield than conventional karyotype, in our institute we are doing microarray instead of a conventional karyotype for all symmetrical IUGR.

Objectives

1. Primary objectives: to evaluate the results of Microarray in symmetrical IUGR babies.
2. Secondary objectives: to compare microarray positive babies and negative babies regarding: gestation age, weight, Apgar score, need and indications for NICU admission as well as the length of NICU of stay.

Study Methodology

This is a retrospective study of symmetrical IUGR babies regarding their microarray results. Babies were considered symmetric IUGR if their growth parameter is less than 10 centiles of gestation age and sex. Symmetrical IUGR babies (as per growth curves) born in Al Wakra hospital, Qatar between Jan 2016 and December 2017 were included in this study. Exclusion criteria were Down syndrome babies, Multiple pregnancies and TORCH+ cases.

Required maternal and neonatal data were extracted from the electronic patient records (Cerner) and entered into the data collection sheet. Maternal data included: age, parity, history of abortions, family history, gestational age, mode of delivery, instrumental use for delivery, meconium stained liquor and fetal deceleration. Newborn data included: weight, sex, Apgar score at 5 minutes, need of resuscitation, cord pH, dysmorphic features or malformations, need for NICU admission, the reason of admission, duration of NICU stay and lab parameters as TORCH, microarray result, leukocyte count, platelet count, ultrasound and ECHO.

Data collection was started after approval from the institutional review board and ethical committee. For data analysis, SPSS 18 was used. Statistical test was applied as appropriate. P value was taken as significant if less than 0.05.

Microarray test method

The test was done by cytogenetic and molecular cytogenetic laboratory, Doha, Qatar. Genome-wide oligonucleotide array-based comparative genomic hybridization (aCGH) analysis was performed with the use of Human Genome CGH Microarray kit (OGT). The array contains ~40,000–180,000 DNA oligonucleotide probes spaced approximately 30–37kb apart genome-wide. The probe sequences and locations were from the human genome build (hg19). The purpose of this experiment was to identify any copy number changes (aneuploidy, gain/loss) associated with a chromosomal abnormality. Interpreted as per the database of Genomic Variants (projects.tcag.ca/variation).

Result

Between Jan 2016 and December 2017 total 10,695 babis babies were born. Among that 578 babis babies were IUGR (501 asymmetric and 77 symmetric IUGR). Total 71 babies were taken in our analysis after excluding 3 down syndrome and 3 babies part of multiple pregnancies. Microarray test had positive findings in 14/71 (19.7%). There were copy number changes of unknown significance in 8/71 (11.2%). In (Table 1) findings are presented. It was abnormal in 2/71 cases (2.8%), one 22q11.2 Deletion syndrome consistent with DiGeorge syndrome had left absent kidney but normal ECHO finding and another one trisomy 18 had atrial septal defect and ventricular septal defect. The latter died on day 18 of life.

Table 1. microarray test result findings in 14 positive cases. Interpreted as per the database of Genomic Variants (projects.tcag.ca/variation).

We had compared maternal outcome in microarray positive result group with negative result group in respect of parity, abortion, mother age, gestation age, delivery mode, instrument used for delivery, meconium stained liquor or fetal deceleration. Findings were not significantly different between groups. see (Table 2).

Table 2.

When compared some neonatal outcomes between the same two groups as sex, birth weight, the need of resuscitation, cord ph., NICU admissions, findings were not significant either. See Table 2. However apgar score at 5 minutes was statistically significant P 0.008 but clinically not significant mean 9.4/ 9.9. Duration of NICU stay was significantly high in microarray result positive group P 0.019, mean duration of stay ±SD 18.14 ±10.6 / 8 ±6.28 day. Among blood counts, platelets were significantly low in microarray positive result group P 0.045, mean ±SD 148 ±83 / 221±99. TORCH was done in 69/71 case and it was negative in all.

Ultrasound (brain ultrasound done in 13 cases and abdomen ultrasound in 13 cases) and ECHO done in 6 cases as clinically indicated; findings are presented in (Table 3).

Table 3. Ultrasound and ECHO result in microarray positive and negative result cases.

Discussion

Neonates affected with Intrauterine growth restriction (IUGR) not only have high prenatal and postnatal complication but also at high risk of cerebral palsy, developmental delay and behavioral dysfunction 4,5. Increasing evidence points to a link between IUGR and adult metabolic syndrome [11, 12].

As in early-onset symmetric IUGR one cause is chromosomal disorder. So, karyotyping is one investigation particularly if a baby is found with abnormal features. As Chromosomal microarray analysis (CMA) is a high resolution, whole-genome screening technique that can identify most of the chromosomal imbalances detected by conventional cytogenetic analysis, as well as smaller submicroscopic deletions and duplications and can be performed on uncultured DNA samples [10]. With accumulating experience during the last decade and data demonstrating improved detection of chromosomal abnormalities compared to conventional karyotyping, CMA is proving to be a valuable diagnostic tool in the prenatal setting [8,9]. Some recommend using CMA for genetic analysis when fetal structural anomalies and/or stillbirth need to be evaluated and to replace the need for fetal karyotype in these cases [10]. But CMA analysis will not detect balanced alterations (reciprocal translocation, inversions, Robertsonian translocations, and balanced insertions).

In our study, chromosomal changes were detected by microarray test in 19.7% (14/71) of symmetric IUGR babies and mostly copy number changes of unknown significance. In two cases it was abnormal 2.8% (2/71), one going with DiGeorge syndrome and another one trisomy 18. The latter died in the neonatal period. We did not find any study in IUGR babies to compare our result. In one study CMV was used as prenatal test for clinical indications as abnormal findings on fetal ultrasound, positive Down syndrome screening or maternal anxiety concerning advanced maternal age, family history of genetic disorder or previous child with anomalies; detected 20% (44/220) clinically significant copy number variants (CNV), of which 21 were common aneuploidies and 23 had other chromosomal imbalances [9]. In another study to see utility of chromosomal microarray in predicting neonatal outcomes in the setting of fetal malformations, they found that nineteen (26.8%) had pathogenic CNV and of these, there were 6 neonatal deaths (31.6%) compared to 8 of 49 (16.3%) in normal CMA cases (p . 0.16) [13]. In another study done on the utility of chromosomal microarray in anomalous fetuses, Abnormal CMA was not associated with increased odds of perinatal death in this cohort and fetal; such fetuses are at high risk of perinatal death irrespective of CMA result [14].

CMA technique does not require dividing cells, in contrast to conventional karyotyping, which requires cell culture, so has quicker turnaround time. CMA has a greater resolution than conventional karyotyping, allowing for the detection of much smaller, submicroscopic deletions, and duplications typically down to a 50- to 100-kb level [15]. The disadvantage of CMA is that it looks for genomic imbalance and is not able to detect totally balanced chromosomal rearrangements, such as translocations or inversions. Also, CMA does not provide information about the chromosomal mechanism of a genetic imbalance ie change is trisomy or an unbalanced Robertsonian translocation which sometimes need to be confirmed by a karyotype. [15,16] Another disadvantage of CMA is the inability to precisely interpret the clinical significance of a previously unreported CNV or to accurately predict the phenotype of some CNVs that are associated with variable outcomes 10. CNVs are characterized as benign, clinically significant (ie, pathogenic), and as a variant of uncertain significance (VUS). The overall prevalence of VUS is approximately 1–2% [17,18,19]. In our study it was in 11.2% symmetric IUGR babies which is much higher than reported prevalence. There is no study in IUGR babies to compare our result.

When comparing microarray positive cases with microarray negative cases, most findings were not significant except the duration of NICU stay and platelet count (Table 2). NICU admission was high among IUGR babies about 50% but comparable in both groups. In one study the overall admission rate was 7.2 per 100 births [20]. Leukopenia and thrombocytopenia are known in IURG. In our study thrombocytopenia was present in 24% (12/50) cases. Among microarray positive group it was in 50% (4/8) babies and in 75% of them (3/4) count was less than 100 x 106. Among microarray negative, thrombocytopenia was in 19% (8/42) and in 37% of them (3/8) count was less than 100 x 106. Platelet count is more significantly low in microarray test positive group 148/221 x 106 cmm P= 0.045. In our study TORCH was negative in all cases. One case with 22q11.2 Deletion syndrome had absent one kidney but normal cardiac anatomy. One case with trisomy 18 had atrial septal defect and ventricular septal defect.

Conclusion

Most of the microarray test results were copy number changes of unknown significance which is comparitively much higher than reported prevalence. Microarray positive IUGR had comparable NICU admissions to negative result group but their duration of stay, initial lower Apgar scores and thrombocytopenia was significantly higher. This may be because even copy number changes have unknown significance, they may have some clinical effect which is not known till now and may need further studies and long term followup for those cases.

This study has been Approved by Medical Research Center Hamad Medical Corporation  # MRC-01-18-006

References

1. Demirci O, Selçuk S, Kumru P, Asoğlu MR, Mahmutoğlu D, et al. (2015) Maternal and fetal risk factors affecting perinatal mortality in early and late fetal growth restriction. Taiwan J Obstet Gynecol 54: 700–704. [crossref]
2. Bernstein IM, Horbar JD, Badger GJ, Ohlsson A, Golan A (2000) Morbidity and mortality among very-low-birth-weight neonates with intrauterine growth restriction. The Vermont Oxford Network. Am J Obstet Gynecol 182: 198–206. [crossref]
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Introduction

Unintended pregnancy (both unplanned and unwanted) is a frequent public health problem worldwide [1,2]. It is estimated that in the Middle East and North Africa (MENA) region, one in four pregnancies is unintended. It is leading to unsafe abortions and jeopardizing the health and wellbeing of women and their families [3,4]. Dramatically, the World Health Organization (WHO) estimated that one woman dies every eight minutes due to unsafe abortion in developing countries [5]. Emergency contraception (EC) plays a vital role in preventing unintended pregnancy on 98% of occasions if applied correctly. Consequently, it helps to reduce unintended childbearing and unsafe abortion, which are major problems affecting maternal health [6]. The knowledge about back-up support and use of emergency contraception (EC) is the most important factor to prevent unplanned or mistimed pregnancies. EC is a type of contraception which is indicated after unprotected intercourse, following sexual abuse, misuse, or nonuse of contraception [7]. It includes the use of Emergency sexual Contraceptive Pills (ESCP), and/or the insertion of Intrauterine Device (IUD) [8]. Emergency contraception pills (ECPs) are also known as ‘the morning after pill’, ‘interception’, ‘post-coital contraception’ or ‘vacation pill’. ECPs include the use of a high dose of combined oral contraceptive pills (COCs) containing Ethinyl estradiol and levonorgestrel (the Yuzpe regimen) or the use of a high dose of Progestin-Only Pills (POPs) containing levonorgestrel. ECPs are effective only if used within 72 hours after unprotected sex. The effectiveness of ECP is 75% in the case of COCs and 85% in the case of POPs. ECPs can prevent pregnancy by delaying or inhibiting ovulation, prevent implantation, fertilization or transport of the sperm or ovum. ECPs do not interrupt or abort an established pregnancy. Once implantation has occurred, ECPs are not any more effective. Thus, ECPs do not cause any form of abortion or bring about menstrual bleeding [9–11]. Insertion of the intrauterine device (IUD) within seven days of unprotected intercourse has been reported as a highly successful method of post-coital emergency contraception. It prevents fertilization through the effect of Cu ions on sperm function and prevents endometrial receptivity [12,13]. Over the past years, contraceptives are available and well known in the Arab region [14]. However, emergency contraception is not widely known, and it not very commonly used. Most of the international studies focused on women’s attitudes towards and barriers to EC use [15,16].⁾ Only a few studies have been conducted in developing countries, especially the Muslim world [17,18]. The first step towards understanding the use of EC is assessing local physicians’ knowledge of the methods and willingness to prescribe them. Based on this, we conducted this study to assess the knowledge, attitudes, and practice of primary health care centers physicians towards EC use.

Literature Review

In a study published from Egypt in 2012, by Shaaban et al., it was found that specialists’ knowledge was significantly high regarding the three most commonly used methods of EC: combined oral contraceptive method, progesterone-only pills (plan B) method and IUCD. The results of this study showed that only 39.5% of obstetrics and gynecology specialists and 24.0% of GPs/family physicians’ specialists and GPs/family physicians had a favorable attitude toward EC. 39.5% of specialists and 26.6% of GPs/family physicians reported ever prescribing EC. The combined oral contraceptive method was the most commonly prescribed method by specialists at 31.5%and GPs/family physicians at 27.0%. Age and years of experience significantly affected the three outcome measures [19].

In a teaching hospital in Karachi, Pakistan 2009, Abdulghani et al. reported that majority (71%) of the Family Physicians, including faculty physician, residents, and medical officers were familiar with emergency contraception, while 42% were not sure about the mechanism. Barriers to EC use were identified as religious/ethical reasons, liability, teratogenicity, and inexperience. Overall attitudes regarding emergency contraception were positive [20]. Another study conducted in 2005 by Sahin et al., in Maternal–Child Health/Family Planning Centers located in the European region of Istanbul, it showed that 82.9% of the family-planning providers including physicians, nurses and midwives were aware of emergency contraception correct description, time of administration and correct dosage of different method. 60% of them had accurately described the intrauterine device as emergency contraception. Most participants reported positive attitudes towards the need and use of Emergency contraception [21].

A study published in Saudi Arabia titled “One other side Emergency contraception: Awareness, attitudes, and barriers of Saudi Arabian Women” by Karim et al., conducted among 242 women showed that only 6.2% of the participants had some knowledge of EC and only two of them had ever used it. Health care professionals were the least reported source of EC information (6.6%, 1/15). The majority (73.3%) had a negative attitude toward EC being available over-the-counter without a prescription. The most common barriers to EC use were concerns about possible health effects. Only two women (13.3%) considered religious belief as a major hindrance to EC use. This study revealed that Health care professionals were the least reported source of information, which is a cause for concern. The major barriers identified for this were concerns of women about the possible side effects of EC and its health consequences. The authors in this study recommended that health care professionals should be encouraged to provide appropriate counseling services related to reproductive health in their consultations tailored to the country-level characteristics, in light of the social norms and religious values [22]. Another study from Saudi Arabia aimed to determine the knowledge, attitude, and practice of EC among Saudi women of childbearing age. This study included 370 women, with a mean age of 32.3 ± 6.3 years. Of these, 31.6% knew how to prevent pregnancy after unprotected sex, and 62 knew about EC, 67.7% thought EC should be widely advertised, and 48.4% thought it should be made available even without a prescription. Almost 76% said that they were not shy to ask for EC, and 59.7% claimed that both partners should decide about the use of EC. The most common reason for not using EC was medical concerns. The authors in this study concluded that among Saudi women, knowledge, awareness, and EC use remain low, although a positive attitude for future EC use exists [23].

In Iran, there is a study that reported EC knowledge and attitude scores of 69.4 ± 11.8 and 70.1 ± 12.8, respectively among health care providers. The providers’ knowledge score was good only in 35%, but the vast majority (95.7%) had positive attitudes [24].

Aims and Objectives

In this study, we aimed to assess the Knowledge, attitude, and practice of health care centers physicians about the commonly used types of emergency contraception (EC) methods.

Methods

This was a descriptive cross-sectional study that has been conducted in 11 Primary Health Care Centers in Prince Sultan Military Medical City, which is in Riyadh at the center of Saudi Arabia. The study population included Primary Health Care Physicians who worked at Prince Sultan Military Medical City, Irrespective of nationality, gender, age or type of education.

The sample size was calculated by using the equation:

With considering a confidence level of 95 and a confidence interval of 5, the total population approximately 182. The sample size was 110, and by adding 10% of non-response or missing data, it ended with a sample size of 121. Data were collected by two methods: self-administered questionnaire and online Google form. The study was conducted during the period from February to March 2019 by using the random sampling technique. The questionnaire was developed by the researcher and supervisor after reviewing the previous similar researches. The questionnaire consists of five parts; the first part includes questions regarding personal data and occupation history (age, gender, marital status, nationality, job position, and the number of years in practice). The second, third, and fourth parts include questions about emergency contraception knowledge, practice, and attitude; respectively. The fifth part includes perceived barriers.

The questionnaire was reviewed for validation by three expert consultants in Family Medicine. A pilot study was done among 20 primary healthcare physicians to assess the understanding of questions and feasibility of the questionnaire. The knowledge questions were scored 1 for a correct answer and 0 for an incorrect answer for each question, and the total knowledge was converted to a percentage score where it was considered satisfactory if > or =70% and unsatisfactory if <70%. For attitude questions; they were scored on a scale from 1 to 5; where strongly disagree was scored 5 and strongly agree was scored 1. The total score was converted to a percentage score where it was considered positive if > or =70% and negative if <70%.

Statistical Analysis

SPSS statistical package for the social sciences software, version 25 was used for the statistical analysis. All categorical variables were expressed as frequencies and percentage and continuous variables were expressed as mean and standard deviation (SD). Appropriate statistics were used for categorical and continuous data, by chi-square and student t-Test. To study the relation between knowledge, attitude, and practice, and also to analyze the relationship between knowledge, attitude and practice of EC with demographic characteristics and occupational history of physicians (e.g.; age, gender, nationality, number of years in practice and area covered by physicians). A p-value of < 0.05 was considered statistically significant.

Results

(Table 1) Slightly more than half (51.2%) of the sample were males, and around half (43.8%) were in the age group of 20–29. Around three quarters (76%) of the sample were Saudi, and (14%) were Egyptians. The majority (66.1%) were married and around a quarter (26%) of the sample were junior residents, while 21.5% were senior registrar, and 19.8% were senior residents. The highest percent (77.7%) had national highest qualification while 13.2% had international, but not Western qualification and only 9.1% had Western qualification. More than half of the sample (54.5%) had 0–5 years of experience, while around the third (28.1%) had 6–10 years. Around half of the sample (43%) had 6–7 sessions per week, while 39.7% had 8–10 sessions, and only 17.4% had 0–5 sessions. More than 94% of the sample mostly cover the General Clinic GB, while 58.7% mostly cover the General Clinic GW. Data is shown in table one.

Table 1. Demographic characteristics and occupational history of the studied physicians (n = 121).

(Table 2) Nearly all (97.5%) of the studied sample heard about EC. The EC method most heard about was the Levonorgestrel method (plan B method) and the IUCD copper method with 84.3% followed by the Levonorgestrel method (split method) with 72.7% while the method least heard about was Ulipristal method with 28.1% of the participants. Data is shown in table two.

Table 2. Hear about emergency contraception and its methods among the studied sample (n = 121).

(Table 3) The most available EC method was the IUCD copper method with 60.3%, while the least available method was the Ulipristal method with only 5%. More than half of the sample was not sure about the availability of all methods. Data is shown in table three.

Table 3. Availability of emergency contraception methods.

(Table 4) The highest scored reasons for EC indication were unprotected sexual intercourse by 90.9%, followed by Condom breakage by 83.5% of the participants. The Majority (77.7%) of the participated physicians know the correct time of initiating oral EC methods, and most of them have never been trained on the use and application of EC. Data is shown in table four.

Table 4. Knowledge about emergency contraception among the studied sample (n = 121).

(Table 5) The results of the current study revealed that more than two thirds (67.8%) of the studied sample had unsatisfactory Knowledge about EC, while only 32.2% (less than the third) had satisfactory knowledge. Data is shown in table five.

Table 5. Levels of Knowledge about emergency contraception among the studied sample (n = 121).

(Table 6) As shown in table six, most (87.6%) of the studied sample had not prescribed EC before, while only 12.4% did so. All those who prescribed EC before have prescribed it rarely. More than half of the sample (53.7%) have never done so, while 28.1% were not sure, and only 18.2% prescribed EC.

Table 6. Practices regarding emergency contraception among the studied sample (n=121).

(Table 7) As shown in table seven, the item with the highest positive attitude was “Are you interested in learning more about EC with 57.9% strongly agree, and 33.9% agree, followed by benefits of EC outweigh the risks with 31.4% strongly agree, and 57% agree. On the other hand, the item with the highest negative attitude was “are you satisfied with your current knowledge of EC” with only 1.7% strongly agree and 13.2% agree. Most of the studied sample (38% strongly agree, and 43% agree) are in favor of the use of EC, and only 19% were either strongly agree or agree that they are uncomfortable with prescribing EC for religious/ethical reasons.

Table 7. Attitude towards emergency contraception among the studied sample (n = 121).

(Table 8) Overall, most (67.8%) of the studied sample had a positive attitude towards EC, while only 32.2% (less than the third) showed a negative attitude, as shown in table eight.

Table 8. Levels of attitude among the studied sample (n = 121).

(Table 9) There was a statistically significant difference between health care providers’ knowledge and attitude towards EC with a p-value of 0.002. Among those with unsatisfactory knowledge level, 41.5% have a negative attitude towards EC, while 58.5% showed a positive attitude. For those with a satisfactory level of knowledge, the percent of negative attitude was only 12.8%, while the majority (87.2%) have a positive attitude. Data is shown in table nine.

Table 9. Relation between knowledge and attitude towards emergency contraception among the studied sample (n=121).

(Table 10) As shown in table ten, there was no statistically significant difference between health care providers’ knowledge and practice of EC with the p-value was 0.2. Among those with satisfactory knowledge level, the percent of prescribed EC was only 17.9%, while the majority (82.1%) have not prescribed EC. For those with unsatisfactory knowledge level, 9.8% prescribed EC while 90.2% have not prescribed EC.

Table 10. The relation between knowledge and practice of emergency contraception among the studied sample (n = 121).

(Table 11) There was no statistically significant difference between health care providers’ practice of and attitude towards EC since the p-value was 0.09. Among those with a Positive attitude, the percent of prescribed EC was only 15.9%, while the majority (84.1%) have not prescribed EC. For those with a negative attitude, 5.1% prescribed EC while 94.9% have not prescribed EC. Data is shown in table eleven.

Table 11. The relation between practice of and attitude towards emergency contraception among the studied sample (n=121).

(Table 12) As shown in table twelve, there was no statistically significant difference in levels of EC Knowledge by gender, nationality, marital status, position in the medical profession, place of highest qualification achieved and area covered by physicians since the p-value was more than 0.05.

Table 12. Demographic characteristics and occupational history According to Knowledge of emergency contraception among the studied sample (n = 121).

(Table 13) As shown in table thirteen, Statistically significant results were obtained between the practice of EC with nationality (chi-square=17.13), position in the medical profession (chi-square=14.34), highest qualification achieved (chi-square=7.72), and area of specialization; being the highest among Non-Saudis, registrars, those with Western high qualifications, covering general walk-in clinic GW (chi-square=8.48), Antenatal clinic ANC (chi-square=7.63), and Well Women Clinic WWC (chi-square=6.40) with p-values of <0.05. The percentage of primary health care physicians who prescribed emergency contraception was 15/121 (12.4%). The percentages of Non-Saudi physicians and Saudi physicians who prescribed EC were 34.5% and 5.4%; respectively.

Table 13. Demographic characteristics and occupational history According to the practice of emergency contraception among the studied sample (n = 121).

(Table 14) There was no statistically significant difference between attitude towards EC by gender, nationality, marital status, position in the medical profession, highest qualification, area covered (general booked clinic GB and Chronic Disease Clinic CDC) age, years and number of sessions per week since all p-values were >0.05. In contrast, a statistically significant difference (p-value <0.05) has been found between attitude towards EC and General Clinic walk-in GW (chi-square=5.40), Antenatal Clinic ANC (chi-square=4.86), Well Women Clinic WWC (chi-square=5.75). The percentages of primary health care physicians with a positive attitude who were covering General walk-in Clinic GW, Antenatal Clinic ANC, Well Women Clinic WWC were 76.1%, 89.5 %, and 100% respectively. Physicians with a positive attitude had a borderline (t-test=1.91, P 0.052) statistically significant higher mean (±SD) of years of experience compared to those with a negative attitude at 8.09+8.279 vs. 5.28+5.730; respectively. Data is shown in table fourteen.

Table 14. Demographic characteristics and occupational history according to attitude towards emergency contraception among the studied sample (n = 121).

(Table 15) The results of the current study highlighted that 82.6% of physicians participated in the current study perceived lack of knowledge as the most important barrier of EC. Cultural issues came in second place with 64.5% while patients’ acceptance was next with 35.5% and side effects of methods with 22.3%. Only 1.7% perceived unavailability in pharmacies as a barrier. Data is shown in table fifteen. Although nearly all the studied sample (97.5%) heard about EC, the knowledge about EC was moderate, with total knowledge of 58.5% and less than one-third of the sample had a satisfactory knowledge level. Participated physicians mostly did not hear about some EC methods such as the Ulipristal method.

Table 15. Perceived barriers of emergency contraception among the studied sample (n = 121).

Discussion

The results of the current study are in accordance with previous similar studies [25–28], which have shown clear gaps in knowledge regarding emergency contraception among healthcare providers, including physicians. This might have an effect on the provision of emergency contraception as they are involved in management, and incomplete knowledge could delay timely scheduling or administration. On the other hand, such findings were in contrast with what has been reported from Lagos [29], where a high degree of awareness of and a largely favorable disposition toward emergency contraceptives among health care providers was shown. The copper IUD, being the most available EC method according to physicians participated in the current study, is by far the most effective option for EC; since a review of 42 studies showed that the pregnancy rate after insertion of the copper IUD for EC is less than 0.1% [30].

Lack of training on the use and application of EC was reported by most of the physicians, which reflects the shortage of dependable information on EC in Saudi Arabia. This was also previously reported from a similar study from Vietnam [31]. It seems that the gaining of EC knowledge during pre- and in-service education of healthcare providers in Saudi Arabia is not served. Additionally, most of the participants reported that they were unsatisfied with their current knowledge of emergency contraception. Proper training is urgently needed to ensure that physicians are knowledgeable enough with different methods of EC to prescribe it when the situation warrants.

A higher knowledge about EC will build up health care professionals’ capacity to provide accurate and effective information on EC to prevent unplanned and unwanted pregnancies. Similar studies in Korea and America found a significantly higher knowledge of EC among participants who had received education and formal content on EC [32,33]. As per the results of the current study, EC still remains a mostly underutilized option in unplanned pregnancy prevention. The knowledge gap is almost the main reason for both the health care providers, which can negatively impact the prescribing habits and future promotion of EC, and this is in agreement with the Sharma C study [34]. In disagreement with the Gupta R et al. study from India [35], the highest proportion of primary care physicians in our study “disagree” on the point “didn’t feel uncomfortable prescribing EC on religious or ethical grounds”.

According to the United Nations Fund for Population Activities (UNFPA 2013), emergency contraception acts on disrupting ovulation and reduces pregnancy likelihood. It cannot prevent fertilized egg implantation, harm a developing embryo, or end a pregnancy. Additionally, according to the WHO, there is no risk on the fetus if a pregnant woman uses the EC. Based on this, performing a pregnancy test before prescribing these methods is not necessary [36]. However, only less than half (46.3%) of participants in the current study had correct knowledge in this regard, a percentage which is considered lower compared to a rate of 67% that was reported by the study of Abdulghani and colleagues in Pakistan [25], and a rate of 94% in an Iranian study [37].

Nonetheless, the majority (79.3%) of the physicians in this study wrongly agreed that EC is an abortifacient which is far higher than the findings of Lee et al. in America [38], and Delaram and Rafie in Iran [39]. These are alarming findings, given that women seeking to use EC depend on healthcare providers for information. This emphasizes the need to broaden and provide detailed education on EC in medical training schools. Moreover, it was worrying about finding that the physicians participated in the current study believed EC use discourage usual contraceptives use, which is against the literature that does not support the argument that EC use discourages the use of other methods of contraception [40].

Compared to a previous similar study from Iran which reported that 95.7% of the health care providers had a positive attitude toward EC, the current study showed that 67.8% of the respondents have a positive attitude towards EC [37]. More than half of the participants felt that emergency contraception was an appropriate topic to discuss at routine consultation, a finding which is in contrast to the Pakistani study, where more than half of the participants felt that EC was not an appropriate topic to discuss at routine consultation. The largest percentage of the participants in our study were not uncomfortable because of religious reasons, which is different from the findings of previous research [25,41,42].

Lower levels of EC prescription have been reported from studies from developing countries. In Nairobi, Kenya, 15% of family-planning service providers reported having prescribed EC [43], and 20% of primary healthcare workers EC in Turkey [44,45]. These rates are even considered high compared to what we found in our study, where the prescription rate was 12.4%.

To the best of our knowledge, this is the first study conducted among primary health care physicians in this regard. Previous studies conducted among Saudi women and came up with the findings that the knowledge, awareness, and use of EC among Saudi women was low; however, a positive attitude for future EC use exists [46,47]. Though it is a small study with small sample size, further larger studies, at regional levels, can identify geographic and demographic gaps in EC practices. It would also be recommended to examine the knowledge and practices of other healthcare providers, including nurses and midwives, who may offer education to patients and communities about emergency contraception.

Conclusion

This study showed clear gaps in emergency contraception (EC) knowledge among primary healthcare physicians. Although the vast majority of the participants heard about EC, their knowledge was moderate, and less than one third had a satisfactory knowledge level. Most of the participants showed a positive attitude towards learning more about EC and also showed that they are with the use of EC. Educational programs that enhance and promote physicians’ awareness of and attitude toward emergency contraception is highly recommended.

Recommendations

Educational efforts should be focused on training of healthcare providers to improve correct access of women and effective use of different emergency contraception methods. Such educational efforts should focus on providing specific knowledge, with particular attention to correct common misconceptions about the EC methods. Providers should be encouraged to inform all potential users about the methods and to prescribe it to clients who require it. Communication about emergency contraception would also provide opportunities for counseling on long-term contraceptive needs. Discussion about emergency contraception should be raised during routine health check-up visits of women. Besides the use of different educational methods to enhance awareness and attitudes of providers, barriers for using EC should be identified, and trials to eliminate them should be done. Future research should be directed at implementing interventions to enhance these types of discussions.

Ethical Considerations

Ethical approval of research conduction from the Research Ethics Committee, Medical Services Department for Armed Forces was approved on 7 MARCH 2018 project no: 1043. It was an amendment and has been approved by the Research Ethics Committee on 09 April 2019. The verbal consent was taken from each physician after explained to them the aim and objectives of the study. The study did not include treatment or intervention for participants. Confidentiality was maintained during data collection and usage.

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