DOI: 10.31038/AWHC.2019262

Abstract

We present a new approach to designing a school and its curriculum. The approach is based upon experimental design of ideas, in which the researcher combines different features of a curriculum, creates a set of vignettes or test combinations, obtains responses from prospective students and others, and then deconstructs the response into the contribution of each individual element or idea. The approach is efficient, cannot be ‘gamed,’ and enables the designer to identify new to the world mind-sets of prospects. These mind-sets entail different ideas about what members of each mindset want. We finish by showing a personal viewpoint identifier, which enables the designer to assign a new person, student, faculty, or donor to one of the mind-sets uncovered. This approach can become a tool for designing a health-professional curriculum.

Introduction

With the increasing costs of medicine in these early decades of the 21^st century, the profession of nursing is undergoing a renaissance. Nurses are becoming recognized as major players in the profession of health care [1, 2]. Nursing has advanced from care to a professional skill in various specialties growing towards a graduate profession status [3, 4]. As with any profession, there is a continuing need to monitor what the desire of customers. This study examines expectations of the public from nursing school. We present the general population with a variety of concepts or ‘vignettes’ about a nursing school, measure their reactions to these vignettes, and then uncover the most appealing concepts per mind-set.

The professional role of nurses is based on the socialization of a set of patterns of behavior. These patterns are embodied in the patient-nurse interactive relationship. The behavior requires skills, knowledge and behaviors based on values, attitudes, guided by the overarching goal of promoting a positive clinical outcome and patient well-being [5]. Nurses assimilate their professional identity through teaching and clinical practice, a combination which generates the ability to cope with the tension between the professional standardization and the nurse’s own individuality and proclivities [6]. Professional education in nursing functions as a disciplinary mechanism design to engender a professional ‘ideology’ and a professional identity as a medical profession stressing the biomedical model [5]. In contrast, the reality of nurses in clinical practice eventuates into what might well be described as shock as they witness traumatic events in the healthcare environment. The unexpected events which occur, often quite frequently, coupled with the individual styles of behavior displayed by senior professional nurses lead to anxiety and dissonance, responses often evident in the published literary discourse regarding the education in Nursing. Anxiety and dissonance are responses to the chasm separating the “theoretical-educational nursing learned in the classroom, the theory as opposed to the reality, the clinical practice experience at hospitals.

There is a looming gap between professional idealism and clinical- practice realism [7, 8]. Professional idealism highlights values of compassion, empathy, holism, cultural competency, and patient-centered care [9, 10]. Reality occasionally eventuates into other behaviors. The ability of nursing students to introject and then realize these values is inhibited by organization constraints and processes, burnout of nurses and staff shortages. All of the former have negative consequences, diminishing the opportunity to develop meaningful relationships with patients, and chipping away at both the satisfaction and sense of mission among nursing students [11]. After completing their education, nursing students were found divide into at least three groups; sustained idealists, compromised idealists and crushed idealists, respectively [12]. There are other effects [8]. views the professional socialization as leading to desensitization toward patients due to experiencing cynicism of senior nurses, and anxiety accompany their efforts to cope with and ameliorate the suffering patients in their care. To preserve themselves in a stressful, demanding at times, chaotic work environment, upon completing their education, nurses adapt emotional desensitization [5, 13]. The idealism of new entrants to Nursing gives way to disillusion after the nurses begin to face an onslaught of never-ending practical concerns [5]. Vulnerable and disoriented nurses, new to the nursing occupation, aspire to “fit in,” resulting in radical changes in understanding, attitudes and behavior towards patients.

Nurses Encounter Three Kinds of Dualism:

1. The good nurse they introjected as themselves versus the bad nurse they encounter in hospitals.
2. their genuineness based on their emotions versus the cynicism they experience as they witness nurses who only care about money
3. An ambiguous identity of authority of the nurses who supervise them, and the lack of morality in the hospital setting.

The Nursing literature concludes that socialization into professional nursing is deeply problematic. Nurses who identify with their professional ideals throughout their education often end up losing professional values, and decline from their ideals to accepting the fact that they compromise, and deliver poor care [14, 15, 16]. The desire to articulate the impact of nursing practice propels professional preparation beyond the existence fuzzy fringes of medicine towards a unique contemporary identity [2].

This study represents an exploration of a method for understanding what the ‘public’ wants in a nursing school. The objective is to create a system to guide education, the system grounded in the feelings of the public towards general, operationally feasible topics and strategies that the nursing school can address. The success of the method (Mind Genomics) has been in understanding the mind of people for a variety of situations. With this success it may be possible for Mind Genomics to contribute to the world of nursing education.

The Background and Contribution of Mind Genomics to Aid Understanding

Mind Genomics is an emerging science, the focus of which is the experimenting science of the everyday. Research tells us a great deal about ‘what is’ but does not tell us how people make decisions about the quotidian, ordinary issues of their daily lives. We know that people have definite opinions about what they want and why they want it; polls and surveys provide us that information. We do not know however, their weighting schemes when they choose what to do. We know what people say, retrospectively, but we do not have a deep knowledge of the decision criteria for the events of the everyday. Rather than asking people to say what they would do Mind Genomics presents people with different combinations of features of a typical situation (here a nursing school) and instructs them to rate the entire combination. From the pattern of their choices for different combinations, Mind Genomics emerges with a set of weights, showing which option(s) or feature(s) in the combinations really ‘drives’ the decision. Through experiment, therefore, Mind Genomics reveals the ‘mind’ in a way that surveys and observations cannot. Mind Genomics shows causality, at least in terms of what the respondent says she or he would do when presented with the type of information one would encounter in a situation. The origin of Mind Genomics comes from statistics experimental design [17] from mathematical psychology conjoint measurement [18] from marketing applications of conjoint measurement [19, 20] and from psychophysical thinking [21]. The foundations have been explicated in several seminal papers, including how the science was founded [22] the major applications [23] and some of the specific mathematics which make the approach and the analyses possible, and actually quite straightforward [24]. The original approaches were patented. Mind-Genomics has been applied to health in several recent studies [25–30].

The Mind Genomics Method

Mind Genomics begins with the Socratic method of question and answer.

Step 1 defines the topic, which is a ‘New Nursing Services Company’

This first step seems so obvious, but it is important to the Mind Genomics experiment that each question and its associated set of answers be relevant to the topic.

Step 2 asks four questions which ‘tell a story’

As simple as that sounds, it requires a great deal of thinking. We are accustomed to facts, not to systematic thinking about a problem. Figure 1 show a screen shot where the researcher is instructed to write out the four questions.

Figure 1. Screen shot showing the screen in the program where the researcher must ask the four questions.

Step 3 Instructs the Researcher to Give Four Different Answers to Each Question

It is the answers which will be presented to the respondent. The answers may either be simple words when the topic is clear and concrete, easy to visualize (here a nursing school), or may be simple phrases when the concept is not clear and concrete, not easy to visualize (e.g., the daily routine of the nursing student in the school.). Figure 2 shows a screen shot of the page where the researcher must provide answers to question #3 (How does it train the students?)

Figure 2. Screen shot showing the page where the researcher provides the four answers to question #3.

The final array of questions and answers appears in Table 1. It will be the answers, in combination, which comprise the test stimuli. The respondent never sees the actual questions motivating the answers. The questions are simply there to create the structure and to motivate the answers.

Table 1. The four questions and the four answers to each question.

Experimental Design and Test Vignettes

The foundation of Mind Genomics is the use of experimental design to combine disparate ideas, present these ideas to the respondent, obtain ratings, and relate the presence/absence of the ideas (also called answers or elements) to the ratings. At first glance one might confuse Mind Genomics with a survey, because a survey instructs the respondent to answer questions. The difference is that Mind Genomics is an experiment. The stimuli are pre-defined, systematic and structured mixtures of messages. The rating is assigned by the respondent based upon the impression of the entire message. The respondent assigns a single rating, mentally weighting the different elements in the vignette. It is the mental weights which are of interest to the researcher, for they reveal what are important features, and what are unimportant features.

The ratings are analyzed by the well-accepted method of OLS (ordinary least-squares, curve fitting) regression. The OLS reveals causality, how the individual elements in the vignettes or combinations ‘drive’ the ratings. It is also worth noting that the same element or answer appears many different times, continually combined with a variety of answers from other questions. This strategy produces, in the words of psychologist William James of Harvard University in the end of the 19^th Century a ‘blooming, buzzing confusion,’ a phrase taken from his description of the world of the baby, but apt for the way the combinations of messages must appear to the respondent. The continual mixing and remixing of messages make it virtually impossible for the respondent to ‘game’ the system. The Mind Genomics experiment reveals quite quickly how the respondent really feels about the combinations, rather than providing answers which might be appropriate and ‘politically correct.’

The experimental design used in this study comprises 24 different vignettes or combinations. Each vignette comprises at most one answer from each question, but in many vignettes one or two questions do not contribute an answer, so that vignette is said to be ‘incomplete.’ The rationale for this strategy is a statistical consideration, namely, to allow the subsequent regression analysis (see below) to provide absolute values for the coefficients.

Every respondent evaluated a unique set of 24 vignettes. Each set was constructed so that the combinations of elements differed, even though the basic structure of the experimental design was maintained. This approach, so-called ‘permutation of the design’[24] allows the Mind Genomics experiment to measure responses to a great number of the possible combinations, albeit with each combination appearing only once or twice across all the respondents. This permutation strategy can be likened to the MRI, which takes ‘pictures’ of underlying tissue from many angles, and combines them by computer to create a three-dimensional image. Each picture in the MRI may be ‘noisy’ just like the data underlying each vignette is ‘noisy.’ Nonetheless, it is the pattern of pictures, and the pattern of responses which emerges clearly in both cases, even though the individual observations are noisy. In contrast, conventional research, specifically conjoint analysis, suppresses the noise through replication, but only covers a limited number of combinations.

A parenthetical note about worldviews in science. Much of science operates on the world view of suppressing noise by replicating the study many times, in order to get a better estimate of the ‘central tendency, i.e., the mean. Inferential statistics tells us that the precision of the measurement increase with the square root of the number of observations. Following this dictum, most researchers opt to increase the number of respondents testing the same stimuli in an experiment or responding to the same questionnaire in a survey. Mind Genomics differs because it looks at the patterns generated by many different combinations, not the response to one combination measured with great precision.

Executing the Mind Genomics study

The actual study takes approximately 3–5 minutes in the field, once the respondent is invited. The effort to run studies is simplified by a service, Luc.id Inc., which hosts on-line surveys, and is linked to the Mind Genomics program as an option. One can also send the study link to other respondents, but the approach of using a panel makes the recruitment more objective, and the panelists more cooperative, since they already do other studies.

The 40 respondents who participated were invited by Luc.id through a link. Most respondents participated within the first hour of launching the study, and all participated within the first two hours, allowing the Mind Genomics experiment to become one step in an easily iterated process. One need not know the answer. One can iterate quickly. The answer, if there is one, appears within the first 1–3 iterations, and certain by the fifth iteration. The key phase is ‘if there is one,’ i.e., if there is a real answer.

The respondent was first presented with an introduction, which simply said: Read our new medical vision. Do you think we will be successful? The respondent then read single vignettes, combinations of elements as shown in Figure 3. The respondent rated the vignette on the 9-point scale. The computer acquired the response, measured the response time between the appearance of the vignette and the rating, and then automatically sequenced to the next vignette. The typical response times are approximately 5 seconds (see Table 2). As just noted above, entire sequence takes 3–5 minutes at most, an experience which is not onerous to those accustomed to surveys lasting 30 minutes but may irritate the purist who wants the respondent to have an experience which is as much fun as a game.

Table 2: Average ratings for total panel, self-defined subgroups, and three emergent mind-sets.

Figure 3. Example of a test vignette, configured for the smart phone.

Transforming the Data

The initial results come in the form of 9-point Likert scales, easy to create and administer, but very difficult to interpret. Indeed, it is not surprising that the standard practice of consumer research is to transform the 9-point rating scale (or other type of rating scale) to a binary scale, more easily understood by managers. Managers who are tasked with the job of ‘doing something with the data’ often ask simple questions like ‘what does an 9 or an 8 mean on the 9-point scale?’ or ‘should I be worried if I got a 5 or lower?’ and so forth.

One ongoing solution to the problem of interpreting the scale simply divides the scale into two parts, often of unequal size. We follow the conventions of consumer research, creating two new scales:

Positive-Outcome

Ratings 1–6 are converted to 0 to denote that these are ‘not positive’ outcomes. They are not ‘Negative-Outcomes,’ but rather just not positive ones. Ratings of 7–9 are converted to 100 to denote that these are ‘positive’ outcomes. The choice of the cut-point is arbitrary, and can be made more stringent by including only ratings of 8 or 9, or even only 9, and less stringent by including ratings such as 6–9, rather than 7–9 as ‘positive.

Negative-Outcome

Ratings of 1–3 are converted to 100 to denote that these are ‘negative’ outcomes. Ratings of 4–9 are converted to 0 to denote that these are not ‘negative’ outcomes. They are not Positive-Outcomes, necessarily, but certainly not Negative-Outcomes. The arbitrary choice holds once again, with possible cut-points being 1–2 as 100, or even only 1 and 100.

A very small random number (<10^–5) is added to each rating in order to ensure that there is some variability within an individual’s ratings, were that individual to limit the ratings to regions where all of their ratings for the vignette would be coded either 0 or 100. Such a situation would cause the individual-level regression modeling to fail. The small random number does not affect the regression model, while ensuring the necessary variation in the dependent variable in order for the regression model to run.

Initial Results – Average Ratings Across Groups

A logical first analysis looks at averages for total and across groups, to determine whether there are any dramatic group to group difference. Table 2 shows the averages for the total panel and for key subgroups as well as three emergent mind-sets to be explicated later. The ratings suggest only a modest level of belief in the success of the enterprise, on average. It will have to be the individual elements which propel success, and not simply the basic idea.

The response time (RT) measured in seconds shows some interesting differences among groups. Females take longer to read the vignettes than do males, on average (2.9 seconds vs 2.2 seconds.) Older respondents take longer to read the vignettes than do younger respondents (4.0 seconds for those age 41+ versus a very fast 1.6 seconds for respondents ages 15–24.)

Despite these differences, we still do not know the relation between the individual elements in the vignettes and the Positive-Outcome, the Negative-Outcome, or the response time, respectively. The analysis of the data by deconstructing the patterns laid out through the experimental design will allow us a far better understanding of the mind of the respondent.

Modeling

The experimental design ensures that the 16 elements or answers are statistically independent of each other. By pooling together all data we generate a database from which we trace ‘causality,’ or the relation between the elements that we put into the vignettes and the responses that individuals make.

The models for the Positive and the Negative-Outcomes are estimated with an additive constant and appear in (Table 3). The additive constant is modest for Positive-Outcome (38) and quite low for Negative-Outcome, respectively (16). We conclude that the respondents feel that it will have to be the element themselves which must do a lot of the work to convince the respondent that there will be a good outcome.

Table 3. Parameters of the equations relating the presence/absence of the 16 elements to Positive-Outcomes (ratings 7–9 converted to 100), Negative-Outcome (rating 1–3 converted to 100), and response time (in seconds.)

When we look at the 16 individual coefficients, we find that there are no elements which strongly drive the Positive-Outcome. For the Negative-Outcome, only two elements really drive additional negativity:

allies closely with community

allies closely with government.

When we look at response time, we do not use an additive constant. The response time does not measure positive or negative, but rather engagement, i.e., time to read and digest the information. The longest response time was 1.3 seconds, supported by public appeals.

Interactions Between ‘Objective Of The Organization’ And Other Elements – Scenario Analysis

The permutation scheme created a large number of different vignettes, with only a few vignettes duplicated. A benefit of this strategy is the ability to uncover interactions between pairs of elements and show how some combinations generate coefficients far higher or far lower than would be expected from looking simply at the performance of the single elements.

Our focus is on how way the different ‘goals of the school’ (answers to question A) ‘interact’ with the remaining elements from the other questions. The process, known as ‘scenario analysis,’ follows these steps:

1. Sort the data set into the five strata, i.e., sets of vignettes. These five strata are where there is no answer from Question A (goal of the organization), and then four remaining strata where the answer appearing in the vignette is A1, A2, A3, and A4, respectively.
2. Run a separate regression analysis on each stratum.
3. The independent variables for each of the five new regression analyses are now the 12 remaining elements or answers, (B1-B4; C1-C4; D1-D4). The variables A1-A4 do not appear in the regression model because they are constant for each regression analysis, and thus are not predictors.
4. The actual analysis is straightforward and shown in Table 4 when the dependent variable is ‘Positive-Outcome,’ Table 5 when the dependent variable is ‘Negative-Outcome,’ and Table 6 when the dependent variable is Response Time. The very strong performing elements are shown by shaded cells, with coefficient values in bold type.
5. Positive-Outcome: Table 4 shows pairs of elements where the stated goal or objective for the school either strongly increases the coefficient of the element (synergism) or strongly suppresses the coefficient of the element (suppression).
6. An example of synergism is the combination of ‘provides local training in homes,’ element C1. In the absence of any objective stated, it generates a coefficient of +7. When combined with the objective ‘to teach’ the coefficient for ‘provides local training in homes’ jumps to +15.

Table 4: Scenario analysis – How the different objectives of the nursing school synergize with other elements to drive the prediction that the outcome will be positive.  Only combinations with strong synergism or suppression are shown.

Table 5: Subgroup models relating the presence/absence of elements to Positive-Outcome.

Key Subgroups

The ability to create models for each individual means that it is easy to create models for pre-defined subgroups. Tables 4–6 show parameters of the models for key subgroups, when the respondents fall into the pre-defined subgroups generated from the self-profiling classification. For this analysis we look at the gender and age, respectively. In the interest of space, we show only those elements which score well in at least one subgroup.

Table 6: The three mind-sets and their reaction to elements in terms of ratings of ‘Positive-Outcome. 

Table 5 (top panel) shows the parameters of the models created for the subgroup when the dependent variable was chosen to be Positive-Outcome. The additive constant provides us with a with a baseline of expected Positive-Outcome in the absence of elements. For the total panel the additive constant is 38, but much higher for females (constant = 48), for younger respondents (age 15–24, constant = 45) and for the oldest respondents (age 41+, constant = 41+). If we were to hazard a rationale for the results it would be that females are basically more interested in the nursing school, as are those contemplating but not yet ready (age 15–24) and those who have made a career decision (age 41+.) Males are less interested, confirming the literature report that there is a dearth of males in professional nursing.

Table 5 (Middle panel) shows the parameters of the models for the subgroups when the dependent variable was chosen to be Negative-Outcome. The additive constants are all low. The highest additive constants are from males (constant = 26) and from age 15–24 (additive constant = 27.) Across the subgroups the key elements driving a predicted Negative-Outcome tends to be ‘allies closely with government,’ and ‘allies with the local community.”

Table 5 (bottom panel) shows the coefficients for the response time model by key subgroups. the longest response times are shaded. The data suggest that the oldest respondents take the longest to process the information. This tends to be a general pattern. It is not clear whether this longer time is because the older respondents take longer to read, longer to comprehend, or longer to respond, or any combination thereof.

Mind Sets

People can be divided by who they ARE, by what they DO, or by their BELIEFS. These ways of dividing people put people into complementary groups with the hope that people in the different groups will think ‘similarly’ about a specific topic. Once groups of people are discovered who ‘think alike,’ they can be efficiently targeted with messages engineered to appeal to them. Conventional research easily creates these clusters of individuals, these segments, based on situational data, behavioral data, or even responses to general questionnaires about a topic. The segments which emerge from these conventional methods are coherent, but only coherent with respect to the measures from which the clusters or segments were derived. People in the same behavioral segment behave similarly on the measures used. People in the same attitudinal or so-called psychographic segment, respond similarly to the general questions [31].

There is a fundamental flaw in most of the segmentation scheme in use today, namely the failure of the ‘top down segmentation’ to be specific and prescriptive at the level of action. The problem of top-down segmentation, dividing people on the basis of general patterns, is the problem of granularity, or more properly the inability of the general segmentation to deal with the granular application, the specific need. When we assume that people in attitudinal or psychographics respond ‘similarly,’ we are dealing with general responses. They may respond quite differently when the topic is far more specific, more granular, and far less general. With the data we have here, a two people might be in the same general segment for education yet respond quite differently when we deal with granular topic of nursing education.

Mind Genomics works at the level of the granular, where everyday life is lived. Rather than looking for these large segments, Mind Genomics operates at the level of specifics, granularity, at the level of the actual questions and answer for the topic. Mind Genomics works from the bottom up, in the manner of a pointillist artist, focusing on the segments which can be uncovered from the granular, individual-level data of a specific project.

The process to discover these Mind-Sets in the population is again quite straightforward, driven by a combination of statistical methods which are ‘objective,’ and interpretation, which is ‘subjective’. The method creates an individual-level model for each respondent, and clusters the models using cluster analysis [32] The results comprise a small number of groups, the so-called clusters, with the property that the patterns of coefficients within a cluster are all similar, whereas the pattern of averages of the coefficients differs dramatically from cluster to cluster. In simple terms, a cluster represents a group of like-minded individuals, based upon the pattern of their coefficients. The individuals are like-minded only with respect to the top of the nursing school. That is the clustering is based upon granular thinking of a specific topic.

The subjectivity of clustering comes when the researcher must decide how many clusters to select, and what to name the clusters for future work. The decision is based upon searching for the smallest number of clusters (parsimony), but with each cluster ‘telling a story’ based upon the pattern of its 16 coefficients (interpretability). These clusters become Mind-Sets in the terminology of Mind Genomics, namely groups which ‘think alike’ in the granular topic of this ‘nursing school.’

The clustering was based on the pattern of coefficients for Positive-Outcome. Once the clusters or mind-sets are established, we can look at the elements which drive Positive-Outcome, as well as Negative-Outcome and Response Time. Tables 6–8 show these three mind-sets, and the elements which drive the three dependent variables, respectively.

Table 7: The three mind-sets and their reaction to elements in terms of ratings of ‘Negative-Outcome.’ The table shows only the elements which drive a strong estimate of ‘Negative-Outcome’ for at least one of the three mind-sets.

Table 8: The three mind-sets and their reaction to elements in terms of Response Time. The table shows only those elements which generate a long response time (>1.4 seconds) for at least one mind-set.

Finding Mind-Sets In the Population

Mind Genomics usually uncovers the different minds in the population. The mind-sets emerge clearly because the input material underlaying the mind-sets are phrases which are ‘cognitively meaningful and rich.’ What does not emerge so quickly is a way to discover these mind-sets in the population. The mind-sets do not distribute by the conventional ways of dividing people, as Table 9 shows, for the distribution of mind-sets by gender and by age, respectively. Even psychographic divisions of people, such as their interest in education and so forth, often do not co-vary with the mind-sets. The mind-sets exist, but it is difficult to assign a new person to the proper mind-set unless the person participates in the research.

Table 9: Distribution of mind-sets by gender and age.

Recently, author Gere has developed a set of algorithms using Monte Carlo simulations, in order to create a set ‘questions’ based on the elements. The pattern of answers to these questions allow the new person to be assigned to the most likely mind-set. The method is called the PVI, the personal viewpoint identifier. It is based on a Monte Carlo simulation, in which the likely pattern of responses to six questions created from six of the elements co-varies with membership in each of the three mind-sets. Figure 4 shows the six questions and the two answers to each question. There are 64 patterns based upon six questions and two answers. Each pattern is most likely with one of the two mind-sets. When a respondent generates a pattern by answering the question the most likely mind-set associated with that pattern becomes the mind-set to which the new person is assigned.

Figure 4. The six question PVI (Personal Viewpoint Identifier) for this study.

Discussion and Conclusions

The traditional topics of curriculum, approaches, and values have been left to the professionals in the field. The role of the ordinary person has generally been to get support for nursing schools (and indeed other types of schools), to fund schools and their programs, and then to quietly hand over the reins of control to professionals. The professional literature is replete with the points of view of professionals about what the curriculum should be, how the student should be taught, and trained to be ready to deliver nursing-care. The general public is often excluded from these discussions. Often, however, it is the general public, or at least those who RECEIVE nursing-care who are the ones able to add most to what is missing. Those involved in teaching, in the world of purveying knowledge, may not realize the changes occurring in their own field and the public expectations from Nursing. Up to now, Nursing has been considered a ‘weak signal,’ rather than the emerging need it really is, to increase patient trust, patient-adherence, patient experiences and patient resilience [33, 34].

Across mind-set segments, the public expects Nursing programs to better prepare nurses by enhancing their professionalism through exposure to more medical settings, as they encounter their clinical practice. For example, the data from the public, non-nursing world, suggests that nursing students should receive clinical practice at doctors’ offices, at local community health centers and at patients’ homes, each beyond the traditional hospital settings. The public also expects local community hospitals to support training of nursing students. Furthermore, the public expects nursing programs to allow researchers to support training and professionalism.  Future studies may test the effect of interventions to adopt the above recommendations on the tensions nursing students experience as they complete their professional education.

Postscript Mind Genomics Research as the Public’s Input and Guide for Educational Institutions

There is an emerging recognition that the fast-changing world of today requires different modalities for learning. Emblemizing this change in the world of textbooks, as an example. The era of heavy, expensive textbooks has gone, or is in the process of departing. These traditional textbooks enjoying two, three, four or more editions, provided a standardized body of knowledge updated regularly. The demands for knowledge are changing, forcing many schools to create their own unique ‘textbooks’ by cobbling together papers available on the Internet, and in the university’s own private collection. The need for creative thinking to develop professional schools for ‘today’ is beginning to be recognized. When we look at the data presented here, the requirements for a nursing school, not from the point of view of the teaching profession but from the point of view of the public, we see these results in a different light.

Acknowledgment

Attila Gere wishes to acknowledge the Premier Post-Doctoral Research Program of the Hungarian Academy of Sciences.

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27. Gabay G, Gere A, Zemel G, Moskowitz D, Shifron R, Moskowitz HR (2019) (b). Expectations and attitudes regarding chronic pain control: An exploration using Mind Genomics. Internal Medicine Research Open Journal 4: 1–10.
28. Gabay G, Gere A, Moskowitz HR (2019) (c). Uncovering communication messages for health promotion: The case of arthritis. Integrated Journal of Orthopedic Traumatology 2: 1–13.
29. Gabay G, Gere A, Moskowitz HR (2019) (d). Understanding effective web messaging – The Case of Menopause. Integrated Gynecology & Obstetrics Journal 2: 1–16.
30. Gabay G, Gere A, Stanley J, Habsburg-Lothringen C, Moskowitz HR (2019) (e). Health threats awareness – Responses to warning messages about Cancer and smartphone usage. Cancer Studies Therapeutics Journal 4: 1–10.
31. Weinstein A and Cahill DJ (2014) Lifestyle market segmentation. Routledge.
32. Dubes RC and Jain AK (1988) Algorithms for clustering data. Prentice Hall.
33. Gabay G (2019) Patient Self-worth and Communication Barriers to Trust of Israeli Patients in Acute-Care Physicians at Public General Hospitals. Qualitative health research Pg No. 1049732319844999.
34. Gabay G (2019) A Nonheroic Cancer Narrative: Body Deterioration, Grief, Disenfranchised Grief, and Growth. OMEGA-Journal of Death and Dying Pg No. 0030222819852836

DOI: 10.31038/AWHC.2019255

Background

Cervical cancer is the second most frequent type of cancer in Cameroon [1]. More than 7.1 million Cameroonian women aged 15 and above are at risk of this disease [2]. In the past ten years, various pilot projects have demonstrated the efficacy and acceptability of Gardasil TM, the vaccine against Human Papillomavirus (HPV) [3,4]. Based on these successful demonstration phases, with the support of the Global Alliance for Vaccines and Immunization (GAVI), the HPV vaccine is finally being implemented countrywide by the Ministry of Public Health. The main objective of the vaccine roll-out will be to immunize at least 80% of 9-year-old girls, and thus reduce the morbidity and mortality due to cervical cancer and other HPV-related infections in Cameroon.

Controversy: social media campaign against HPV

In Cameroon, as in other countries, social media has become a powerful instrument for public communication, allowing users to initiate, modify, share and discuss personal experiences, information, opinions, images, video clips and just about everything online [5]. Although this novel communication channel can be an invaluable tool for networking and visibility on the global arena, it also carries the great potential of heavily negative influence on health campaigns. Studies have shown that false and unverified information is propagated on popular social media platforms such as Facebook, WhatsApp, Twitter, Instagram, Snapchat, with far-reaching and long-lasting deleterious impact on the health of our communities [6].

The launching of the HPV vaccine in Cameroon on 29 October 2014 did not raise as much controversy as the recent announcement by the Minister of Public Health on the decision to finally implement the use of the vaccine to the general population in the coming months. The recent fiery debate emanates from various anti-HPV vaccine campaigns carried out mainly on WhatsApp and Facebook that highlight adverse effects such as sterility that allegedly occurred in Japan and other countries [7].

Although Cameroonian parents are generally more inclined towards vaccinating their children [8], contrary social media campaigns are likely to negatively affect the government’s efforts to implement the use of this HPV vaccine for young girls. Studies have shown that parents and teenagers who have been exposed to anti-HPV vaccine messages on social media are more likely to remember the harmful effects than the health benefits reported [9]. More so, parents who listen to the negative stories are more likely to delay or even refuse the vaccination of their children [9]. Many Cameroonians have experienced the destructive effect of public scrutiny through social media in the face of national events and tragedies. In these instances, the narratives received by the public were almost completely controlled by social media, making it nearly impossible for the government to control the situations and protect its citizens. In the case of the HPV vaccine, social media videos, images and text messages are feeding the community with unsubstantiated myths and misinformation. This anti-campaign undermines the government’s efforts, in concertation with international health communities, to diligently work towards curbing the progress of cervical cancer in the country. On 13 September 2019, a press conference was held by experts from the Ministry of Public Health and the Association of Science Journalists and Communicators for Health Promotion in Cameroon in order to dispel the ambiguities around the controversies on the large-scale implementation of the HPV vaccine in the country.

Cultural and religious beliefs

Culture is known to weigh heavily on health behavior. It affects the perception of health, illness, beliefs about the causes of illness, approaches to health promotion, vaccination, experiences and expressions of disease, places where and people to whom patients go for help, and the type of care that patients prefer [10]. The videos widely spread through social media may promote cultural prejudices that suggest that vaccination is intended to sterilize children [8]. One of these videos cited some studies conducted in Japan and in other countries, alleging that sterility was among the adverse events highlighted in Japan [7]. It is thus not surprising that the planned implementation of HPV vaccination for young girls in Cameroon runs the risk of being rejected if effective community sensitization on the necessity of the vaccine is not carried out.

Conclusion and Recommendation

Vaccination against the Human Papillomavirus is an effective approach for the primary prevention and reduction of the burden of cervical cancer among young girls. The Cameroonian scientific community thus has the impetus to counteract the greatly negative influence of social media on the subject by setting the records straight through presenting empirical and scientific evidence that will leave no grounds for these myths and misinformation.

References

1. Cameroon Human Papillomavirus and Related Cancers, Fact Sheet 2018 (2019) https://hpvcentre.net/statistics/reports/CMR_FS.pdf. Accessed on 03 October 2019.
2. HPV Information Centre – HPVIC (2019) Cameroon Human Papillomavirus and Other Related Diseases Report.
3. Ministère de la Santé Publique (2013) Yaoundé Cancer Registry.
4. Ogembo JG, Manga S, Nulah K, Foglabenchi LH, Perlman S, et al. (2014) Achieving high uptake of human papillomavirus vaccine in Cameroon: Lessons learned in overcoming challenges. Vaccine 32: 4399–403. [crossref]
5. Hao J, Gao B (2017) Advantages and Disadvantages for Nurses of Using Social Media. Journal of Primary Health Care and General Practice 1: 001.
6. Katherine J, Igoe (2019) Establishing the Truth: Vaccines, Social Media, and the Spread of Misinformation. Harvard School of Public Health. https://www.hsph.harvard.edu/ecpe/vaccines-social-media-spread-misinformation/. Accessed on 02 October 2019.
7. Okuhara (2019) Newspaper coverage before and after the HPV vaccination crisis began in Japan: a text mining analysis. BMC Public Health 19: 770.
8. Kwedi Nolna S, Bonono CR, Nsangou Moncher M, Bindé T, Nolna D, et al. (2018) Factors influencing the performance of routine immunization in urban areas: A comparative case study of two cities in Cameroon: Douala and Yaoundé. Vaccine 36: 7549–7555. [crossref]
9. Margolis MA, Brewer NT, Shah PD, Calo WA, Gilkey MB (2019) Stories about HPV vaccine in social media, traditional media, and conversations. Prev Med 118: 251–256. [crossref]
10. White, Peter (2015) The concept of diseases and health care in African traditional religion in Ghana. HTS Theological Studies 71: 01–07.

DOI: 10.31038/AWHC.2019261

 

Midlife women are experiencing increased rates of opioid use disorder (OUD) [1] and fatal overdose in the United States (US) [2]. Rates of opioid-related mortality have increased more rapidly among women than men [3], and when stratified by age, the increase in opioid-related mortality is particularly notable among middle-aged women [4]. Unique risk factors for opioid misuse and opioid use disorder (OUD) in women have been identified, including higher pain levels and increased opioid withdrawal symptoms [5] among women with OUD and higher rates of psychological comorbidities [6,7]. In addition, midlife women with co-occurring chronic pain and menopausal symptoms experience increased rates of risky opioid use including long-term opioid use,  high-dose opioid use, and co-prescription of CNS-depressants and opioids [8]. Thus, addressing pain, menopausal symptoms, and psychological comorbidities may help reduce rates of OUD and ultimately prevent fatal overdose among midlife women.

Complementary therapies (e.g., acupuncture, massage, and mindfulness) and an integrative approach to care (e.g., relationship-based, patient-centered, strengths-based coaching) are ideally poised to treat OUD and associated comorbidities [9]. As women utilize healthcare in greater rates than men [10], including complementary therapies, it seems plausible that the integration of IH modalities may improve women’s outcomes. Of particular interest is the evidence supporting acupuncture as a modality that may decrease psychological distress and therefore reduce OUD and the potential for overdose in women [11]. This brief commentary will provide an overview of the topic of midlife women and OUD, describe the relevance of complementary therapies and a person-centered, relationship-based approach to treatment, and provide suggestions relevant to future policy initiatives and research.

Compared to men, women experience more pain-related disease, have an increased sensitivity to pain [12], are more likely to be prescribed opioids [13], are more likely to “telescope” from use to misuse and first admission to treatment [1], and are at greater risk than men for the misuse of prescription opioid medications and thus for the development of OUD. Sex and gender differences occur in opioid risk and risk mitigation: rates of heroin use have increased at a faster rate while rates of nonmedical prescription opioid use have declined at a slower rate among women compared to men [14]. Women have also seen a sharper increase in opioid-related mortality than men [3]. Between 1999 and 2010, mortality rates increased by 400% for women and 237% for men [3]. Among persons with OUD, women experience a greater risk of mortality compared to the general population (SMR 5.1 95% CI: 4.5, 5.7) than men (SMR 4.3 95% CI: 4.0, 4.6), with an 18% increased risk of death among women compared to men (RR 1.18, 95% CI 1.02–1.36) [2]. When stratified by age, the increase in opioid-related mortality is particularly notable among middle-aged women; between 1999 and 2017, fatal opioid overdose rates increased by 485% among women aged 30–64 years [4]. Thus, identifying novel ways to promote health and prevent OUD among midlife women is critical.

Psychological and emotional distress have been identified as risk factors for OUD among women but not among men [15]. Research indicates that OUD is associated with intimate partner violence victimization, particularly among women, and that women may be particularly susceptible to such violence when under the influence of opioids [16]. Further, opioid-dependent women are more likely than their male counterparts to report higher levels of psychiatric morbidity (e.g., depression and anxiety), to use opioids in response to interpersonal stress, and to use opioids because of affective distress [6,7,15]. Post traumatic stress disorder (PTSD) is more strongly associated with opioid misuse and OUD among women than men. Of note, the desire to avoid symptoms of PTSD has been associated with higher odds of opioid misuse and OUD among women [17].

An integrative approach to care is uniquely situated to address the complex biopsychosocial aspects that fuel the chronic pain syndrome as well as provides care that is situated in a feminist praxis that values mutuality, growth and nurturance, shared power, reciprocity, and individualism. Integrative healthcare (IH) “reaffirms the importance of the relationship between practitioner and patient, focuses on the whole person, is informed by evidence, and makes use of all appropriate therapeutic approaches, healthcare professionals and disciplines to achieve optimal health and healing.” [18] IH includes both conventional or biomedical interventions (provided by physicians, nurses, pharmacists and others) as well as complementary therapies provided by credentialed therapists (i.e., naturopaths, herbalists, acupuncture/Traditional Chinese Medicine practitioners, and massage therapists). In an integrative health approach, plans to address pain would be developed collaboratively and tailored to address whole person (body-mind-spirit) condition, available resources (financial, material, human), and their values/goals. Rather than treating all chronic pain as similar and all clients seeking care as identical, the integrative approach listens deeply to the woman’s story, ensures that the authentic full voice of the person is heard, considers all aspects of the pain experience (benefits as well as limitations) and seeking solutions that are informed by evidence but selected in a manner that affirms the woman’s life purpose and values [19]. Therefore, rather than prescribing an opioid to address chronic pain, an integrative approach might include a complex intervention that includes progressive meditative movement (yoga or tai chi), nutritional changes (anti-inflammatory diet), and acupuncture that would address the complex mind-body mechanism associated with chronic pain [20].

Complementary therapies such as acupuncture, massage therapy, and mindfulness have been found to reduce OUD and associated comorbidities. Acupuncture has been evaluated for the treatment of pain and reduction of OUD in several studies. A meta-analysis was conducted across nine studies and acupuncture and electro-acupuncture were each more beneficial than sham acupuncture for the reduction of cravings for opioids and for the improvement of insomnia and depression [21]. Preliminary evidence suggests that acupuncture may also be beneficial in decreasing opioid dose and increasing opioid abstinence when combined with medication-assisted treatment (MAT) [22]. Further, acupuncture may offer decreased odds of opioid initiation. Compared to visiting a primary care provider, individuals with new-onset low back pain who visit an acupuncturist first demonstrate 91% decreased odds of short-term opioid use (95%CI 0.07 to 0.12) and 95% decreased odds of long-term opioid use (0.07, 95%CI 0.01 to 0.48) [23]. Mindful Awareness Body-oriented Therapy (MABT) has also demonstrated preliminary feasibility and acceptability when provided as an adjunct to MAT for those with OUD and has demonstrated decreased craving among women previously diagnosed with substance use disorder [24].  Additionally, Mindfulness Oriented Recovery Enhancement (MORE) reduces opioid craving among individuals with OUD taking MAT [25], and increases positive psychological health, as well [26].

 Multiple large-scale surveys of complementary and alternative medicine (CAM) ^[1] use in the United States (US) have been conducted, including data from the 2002, 2007, and 2012 Adult Alternative Medicine supplement to the National Health Interview Surveys [27]. Among US adults, “the prevalence of CAM use in the past 12 months” ranged from 32.3% in 2002 to 35.5% in 2007 and was most recently 33.2% in 2012. Among women seeking acupuncture, for example, nationwide prevalence is low (1.1%); however that translates to over one million American women. In addition to seeking IH services from a provider, women are utilizing IH independently. For example, women in the US are already practicing meditation more than men and presence of pain, anxiety/depression, and sleeping problems are some of the main factors that predict meditation use [28]. This indicates that IH is not only acceptable to women but also sought-after by those who have some of the very issues associated with opioid misuse and OUD among midlife women.

Since IH interventions such as acupuncture, MABT and MORE are promising in the treatment of OUD and associated comorbidities and since women are utilizing complementary therapies, it would seem beneficial to women for healthcare policy to support women’s affordable access to complementary therapies. Suggestions regarding policy include the provision of healthcare benefit coverage of complementary therapies, however the most recent data (from 2012) indicate that only a  minority of people in the US have health insurance coverage for complementary therapies such as acupuncture (20% of respondents reported at least partial coverage) and massage (15%) [29]. Because research findings regarding complementary therapies are likely to inform policy changes, additional research is needed to clarify the combined benefits of complementary therapies on pain, mental health conditions, and other symptoms on opioid use and concurrent use of medications such as benzodiazepines for anxiety, which are associated with higher rates of fatal opioid overdose. Further, additional studies of complementary therapies efficacy and effectiveness to reduce rates of OUD and associated comorbidities are needed to then translate those findings to real-world effectiveness studies. We encourage future work in these directions that might result in improved outcomes for women with OUD.

References

1. Back SE, Payne RL, Wahlquist AH, Carter RE, Stroud Z, Haynes L, Hillhouse M, Brady KT, Ling W. et al. (2011) Comparative Profiles of Men and Women with Opioid Dependence: Results from a National Multisite Effectiveness Trial. The American Journal of Drug and Alcohol Abuse 37: 313–323.
2. Evans E, Kelleghan A, Li L, Li L, Min J, et al. (2015) Gender differences in mortality among treated opioid dependent patients. Drug Alcohol Depend155: 228–235.
3. Vital signs (2013) overdoses of prescription opioid pain relievers and other drugs among women–United States, 1999-2010. MMWR Morbidity and mortality weekly report 62: 537–542.
4. VanHouten JP, Rudd RA, Ballesteros MF, Mack KA (2019) Drug Overdose Deaths Among Women Aged 30–64 Years – United States, 1999–2017. MMWR Morb Mortal Wkly Rep 68: 1–5. [crossref]
5. Huhn AS, Tompkins DA, Campbell CM, Dunn KE (2019) Individuals with Chronic Pain Who Misuse Prescription Opioids Report Sex-Based Differences in Pain and Opioid Withdrawal. Pain Med. [crossref]
6. Jamison RN, Butler SF, Budman SH, Edwards RR, Wasan AD (2010) Gender differences in risk factors for aberrant prescription opioid use. J Pain 11: 312–320. [crossref]
7. Huhn AS, Berry MS, Dunn KE (2019) Review: Sex-Based Differences in Treatment Outcomes for Persons With Opioid Use Disorder. Am J Addict 28: 246–261. [crossref]
8. Gibson CJ, Li Y, Huang AJ, Rife T, Seal KH (2019) Menopausal Symptoms and Higher Risk Opioid Prescribing in a National Sample of Women Veterans with Chronic Pain. J Gen Intern Med.
9. Services USDfHaH. Draft Report on Pain Management Best Practices: Updates, Gaps, Inconsistencies, and Recommendations (2019) https://www.hhs.gov/ash/advisory-committees/pain/reports/2018-12-draft-report-on-updates-gaps-inconsistencies-recommendations/index.html#1-introduction.
10. The Use of Complementary and Alternative Medicine in the United States. Accessed from: https://nccih.nih.gov/research/statistics/2007/camsurvey_fs1.htm#most.%20last%20accessed%2010/19/19.
11. Gary Deng, Jun J Mao (2019) Acupuncture to Reduce Opioid Consumption in Patients with Pain: Getting to the Right Points, Pain Medicine 20: 207–208.
12. Pieretti S, Di Giannuario A, Di Giovannandrea R, Marzoli F, Piccaro G, et al. (2016) Gender differences in pain and its relief. Ann Ist Super Sanita 52: 184–189. [crossref]
13. Frenk SM, Porter KS, Paulozzi LJ (2015) Prescription opioid analgesic use among adults: United States, 1999-2012. NCHS Data Brief : 1–8. [crossref]
14. Marsh JC, Park K, Lin YA, Bersamira C (2018) Gender differences in trends for heroin use and nonmedical prescription opioid use, 2007-2014. J Subst Abuse Treat 87: 79–85. [crossref]
15. Back SE, Lawson KM, Singleton LM, Brady KT (2011) Characteristics and correlates of men and women with prescription opioid dependence. Addict Behav 36: 829–834.
16. Smith PH, Homish GG, Leonard KE, Cornelius JR (2012) Intimate Partner Violence and Specific Substance Use Disorders: Findings from the National Epidemiologic Survey on Alcohol and Related Conditions. Psychol Addict Behav 26: 236–245.
17. Smith KZ, Smith PH, Cercone SA, McKee SA, Homish GG (2016) Past year non-medical opioid use and abuse and PTSD diagnosis: Interactions with sex and associations with symptom clusters. Addict Behav 58: 167–174.
18. Health ACfIMa. Definition of integrative medicine and health.  http://www.imconsortium.org/about/about-us.cfm. Accessed 10/8/19.
19. Koithan M (2015) The Promise of Integrative Nursing. Creat Nurs 21: 193–199. [crossref]
20. Thompson S, Wagner (2019) J Integrative nursing management of pain In: Kreitzer MJ, Koithan M, (eds.). Integrative Nursing. 2^nd edn. New York, NY: Oxford Press.
21. Chen Z, Wang Y, Wang R, Xie J, Ren Y (2018) Efficacy of Acupuncture for Treating Opioid Use Disorder in Adults: A Systematic Review and Meta-Analysis. Evid Based Complement Alternat Med 2018: 3724708. [crossref]
22. Zhou K, Jia P, Bhargava S, Zhang Y, Reza T, et al. (2017) Opioid tapering in patients with prescription opioid use disorder: A retrospective study. Scand J Pain 17: 167–173. [crossref]
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29. Nahin RL, Barnes PM, Stussman BJ (2016) Insurance Coverage for Complementary Health Approaches Among Adult Users: United States, 2002 and 2012. NCHS Data Brief Pg No: 1–8. [crossref]

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^[1] “CAM” is a term that was previously used to identify interventions such as acupuncture, massage, and mindfulness and the commonly used term now is “complementary therapies.”