The Primary Care and Environmental Health e-Learning Course to Integrate Environmental Health in General Practice: Before-and-After Feasibility Study
Bibliographic record
Abstract
Background Environmental and behavioral factors are responsible for 12.6 million deaths annually and contribute to 25% of deaths and chronic diseases worldwide. Through the One Health initiative, the World Health Organization and other international health organizations plan to improve these indicators to create healthier environments by 2030. To meet this challenge, training primary care professionals should be the priority of national policies. General practitioners (GPs) are ready to become involved but need in-depth training to gain and apply environmental health (EH) knowledge to their practice. In response, we designed the Primary Care Environment and Health (PCEH) online course in partnership with the Occitanie Regional Health Agency in France. This course was used to train GP residents from the Montpelier-Nimes Faculty of Medicine in EH knowledge. The course was organized in 2 successive parts: (1) an asynchronous e-learning modular course focusing on EH knowledge and tools and (2) 1 day of face-to-face sessions. Objective This study assessed the impact of the e-learning component of the PCEH course on participants’ satisfaction, knowledge, and behavior changes toward EH. Methods This was a pilot before-and-after study. Four modules were available in the 6-hour e-learning course: introduction to EH, population-based approach (mapping tools and resources), clinical cases, and communication tools. From August to September 2021, we recruited first-year GP residents from the University of Montpellier (N=130). Participants’ satisfaction, knowledge improvements for 19 EH risks, procedure to report EH risks to health authorities online, and behavior change (to consider the possible effects of the environment on their own and their patients’ health) were assessed using self-reported questionnaires on a Likert scale (1-5). Paired Student t tests and the McNemar χ2 test were used to compare quantitative and qualitative variables, respectively, before and after the course. Results A total of 74 GP residents completed the e-learning and answered the pre- and posttest questionnaires. The mean satisfaction score was 4.0 (SD 0.9) out of 5. Knowledge scores of EH risks increased significantly after the e-learning course, with a mean difference of 30% (P<.001) for all items. Behavioral scores improved significantly by 18% for the participant’s health and by 26% for patients’ health (P<.001). These improvements did not vary significantly according to participant characteristics (eg, sex, children, place of work). Conclusions The e-learning course improved knowledge and behavior related to EH. Further studies are needed to assess the impact of the PCEH course on clinical practice and potential benefits for patients. This course was designed to serve as a knowledge base that could be reused each year with a view toward sustainability. This course will integrate new modules and will be adapted to the evolution of EH status indicators and target population needs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".