Innovative levers for sustainable integration of gender medicine into medical school curricula

Efforts to integrate gender medicine into medical school curricula have focused largely on the work of individual champions. Online sex and gender materials for undergraduate courses have also been developed and disseminated. Success has been sporadic, with varying uptake across schools within and between countries. International trends in medical school accreditation processes and the growing force of the millennial student voice offer untapped opportunities to promote more systematic integration of gender medicine on a national and international level. In this commentary, the president and CEO of the Association of Faculties of Medicine of Canada and the Scientific Director of the Institute of Gender and Health of the Canadian Institutes of Health Research jointly reflect on top-down and bottom-up levers for sustainable innovation in gender medicine for undergraduate medical training.     

Auditing sex- and gender-based medicine (SGBM) content in medical school curriculum: a student scholar model

Background

Sex- and gender-based medicine (SGBM) aims to (1) delineate and investigate sex- and gender-based differences in health, disease, and response to treatment and (2) apply that knowledge to clinical care to improve the health of both women and men. However, the integration of SGBM into medical school curricula is often haphazard and poorly defined; schools often do not know the current status of SGBM content in their curricula, even if they are committed to addressing gaps and improving SGBM delivery. Therefore, complete auditing and accounting of SGBM content in the existing medical school curriculum is necessary to determine the baseline status and prepare for successful integration of SGBM content into that curriculum.

Methods

A review of course syllabi and lecture objectives as well as a targeted data analysis of the Curriculum Management and Information Tool (CurrMIT) were completed prior to a real-time curriculum audit. Subsequently, six “student scholars,” three first-year and three second-year medical students, were recruited and trained to audit the first 2 years of the medical school curriculum for SGBM content, thus completing an audit for both of the pre-clinical years simultaneously. A qualitative analysis and a post-audit comparative analysis were completed to assess the level of SGBM instruction at our institution.

Results

The review of syllabi and the CurrMIT data analysis did not generate a meaningful catalogue of SGBM content in the curriculum; most of the content identified specifically targeted women’s or men’s health topics and not sex- or gender-based differences. The real-time student audit of the existing curriculum at Texas Tech revealed that most of the SGBM material was focused on the physiological/anatomical sex differences or gender differences in disease prevalence, with minimal coverage of sex- or gender-based differences in diagnosis, prognosis, treatment, and outcomes.

Conclusions

The real-time student scholar audit was effective in identifying SGBM content in the existing medical school curriculum that was not possible with a retrospective review of course syllabi and lecture objectives or curriculum databases such as the CurrMIT. The audit results revealed the need for improved efforts to teach SGBM topics in our school’s pre-clinical curriculum.

     

eGender—from e-Learning to e-Research: a web-based interactive knowledge-sharing platform for sex- and gender-specific medical education

Background

Sex and Gender Medicine is a novel discipline that provides equitable medical care for society and improves outcomes for both male and female patients. The integration of sex- and gender-specific knowledge into medical curricula is limited due to adequate learning material, systematic teacher training and an innovative communication strategy. We aimed at initiating an e-learning and knowledge-sharing platform for Sex and Gender Medicine, the eGender platform (http://egender.charite.de), to ensure that future doctors and health professionals will have adequate knowledge and communication skills on sex and gender differences in order to make informed decisions for their patients.

Methods

The web-based eGender knowledge-sharing platform was designed to support the blended learning pedagogical teaching concept and follows the didactic concept of constructivism. Learning materials developed by Sex and Gender Medicine experts of seven universities have been used as the basis for the new learning tools. The content of these tools is patient-centered and provides add-on information on gender-sensitive aspects of diseases. The structural part of eGender was designed and developed using the open source e-learning platform Moodle. The eGender platform comprises an English and a German version of e-learning modules: one focusing on basic knowledge and seven on specific medical disciplines. Each module consists of several courses corresponding to a disease or symptom complex. Self-organized learning has to be managed by using different learning tools, e.g., texts and audiovisual material, tools for online communication and collaborative work.

Results

More than 90 users from Europe registered for the eGender Medicine learning modules. The most frequently accessed module was “Gender Medicine—Basics” and the users favored discussion forums. These e-learning modules fulfill the quality criteria for higher education and are used within the elective Master Module “Gender Medicine—Basics” implemented into the accredited Master of Public Health at Charité—Berlin.

Conclusions

The eGender platform is a flexible and user-friendly electronical knowledge-sharing platform providing evidence-based high-quality learning material used by a growing number of registered users. The eGender Medicine learning modules could be key in the reform of medical curricula to integrate Sex and Gender Medicine into the education of health professionals.

     

Modelling the spatial distribution of wildlife animals using presence and absence data

This study was conducted to analyze the habitat preference of six major mammals for various environmental factors based on 100 random points within a mountain area in South Korea. In-situ presence and absence data for the mammals were surveyed and collected, and twelve explanatory variables related to topography, water, greenness, and anthropogenic influence were applied to create a habitat distribution model. The best combination of variables was determined using Moran’s I coefficients and Akaike criteria information, and applied to estimate the habitat preference for each species using GRASP v.3.0. The predictive map showed that wildlife animals in this study were mainly categorized into two groups: Group I (Korean squirrel, Sciurus vulgaris, mole, Talpa micrura and water deer, Hydropotes inermis), showed equal preference for all mountainous areas; Group II (weasel, Mustela sibirica, leopard cat, Felis bengalensis and raccoon dog, Nyctereutes procyonoides) showed different preferences in a mountain.     

Barcoding Eophila crodabepis sp. nov. (Annelida,Oligochaeta, Lumbricidae), a Large Stripy Earthworm from Alpine Foothills of Northeastern Italy Similar to Eophila tellinii (Rosa, 1888)

A new Italian earthworm morphologically close to the similarly large and anecic Eophila tellinii (Rosa, 1888) is described. Distribution of Eophila crodabepis sp. nov. extends over 750 km² from East to West on the Asiago Plateau and Vittorio Veneto Hills, from North to South on mounts Belluno Prealps (Praderadego and Cesen), Asiago, Grappa and onto the Montello foothills. This range abuts that of Eophila tellinii in northern Friuli Venezia Giulia region. Known localities of both E. tellinii and E.crodabepis sp. nov. are mapped. mtDNA barcoding definitively separates the new western species from classical Eophila tellinii (Rosa, 1888).     

Link Prediction in Criminal Networks: A Tool for Criminal Intelligence Analysis

The problem of link prediction has recently received increasing attention from scholars in network science. In social network analysis, one of its aims is to recover missing links, namely connections among actors which are likely to exist but have not been reported because data are incomplete or subject to various types of uncertainty. In the field of criminal investigations, problems of incomplete information are encountered almost by definition, given the obvious anti-detection strategies set up by criminals and the limited investigative resources. In this paper, we work on a specific dataset obtained from a real investigation, and we propose a strategy to identify missing links in a criminal network on the basis of the topological analysis of the links classified as marginal, i.e. removed during the investigation procedure. The main assumption is that missing links should have opposite features with respect to marginal ones. Measures of node similarity turn out to provide the best characterization in this sense. The inspection of the judicial source documents confirms that the predicted links, in most instances, do relate actors with large likelihood of co-participation in illicit activities.     

Markov Model Predicts Changes in STH Prevalence during Control Activities Even with a Reduced Amount of Baseline Information

Background

Estimating the reduction in levels of infection during implementation of soil-transmitted helminth (STH) control programmes is important to measure their performance and to plan interventions. Markov modelling techniques have been used with some success to predict changes in STH prevalence following treatment in Viet Nam. The model is stationary and to date, the prediction has been obtained by calculating the transition probabilities between the different classes of intensity following the first year of drug distribution and assuming that these remain constant in subsequent years. However, to run this model longitudinal parasitological data (including intensity of infection) are required for two consecutive years from at least 200 individuals. Since this amount of data is not often available from STH control programmes, the possible application of the model in control programme is limited. The present study aimed to address this issue by adapting the existing Markov model to allow its application when a more limited amount of data is available and to test the predictive capacities of these simplified models.

Method

We analysed data from field studies conducted with different combination of three parameters: (i) the frequency of drug administration; (ii) the drug distributed; and (iii) the target treatment population (entire population or school-aged children only). This analysis allowed us to define 10 sets of standard transition probabilities to be used to predict prevalence changes when only baseline data are available (simplified model 1). We also formulated three equations (one for each STH parasite) to calculate the predicted prevalence of the different classes of intensity from the total prevalence. These equations allowed us to design a simplified model (SM2) to obtain predictions when the classes of intensity at baseline were not known. To evaluate the performance of the simplified models, we collected data from the scientific literature on changes in STH prevalence during the implementation of 26 control programmes in 16 countries. Using the baseline data observed, we applied the simplified models and predicted the onward prevalence of STH infection at each time-point for which programme data were available. We then compared the output from the model with the observed data from the programme.

Results

The comparison between the model-predicted prevalence and the observed values demonstrated a good accuracy of the predictions. In 77% of cases the original model predicted a prevalence within five absolute percentage points from the observed figure, for the simplified model one in 69% of cases and for the simplified model two in 60% of cases. We consider that the STH Markov model described here could be an important tool for programme managers to monitor the progress of their control programmes and to select the appropriate intervention. We also developed, and made freely available online, a software tool to enable the use of the STH Markov model by personnel with limited knowledge of mathematical models.