An e-Learning Course to Train General Practitioners in Planetary Health: Pilot Intervention Study
Bibliographic record
Abstract
BACKGROUND: According to the World Health Organization, climate and ecological emergencies are already major threats to human health. Unabated climate change will cause 3.4 million deaths per year by the end of the century, and health-related deaths in the population aged ≥65 years will increase by 1540%. Planetary health (PH) is based on the understanding that human health and human civilization depend on flourishing natural systems and the wise stewardship of those natural systems. Health care systems collectively produce global emissions equivalent to those of the fifth largest country on earth, and they should take steps to reduce their environmental impact. Primary care in France accounts for 23% of greenhouse gas emissions in the health care sector. General practitioners (GPs) have an important role in PH. The course offers first-year GP residents of the Montpellier-Nîmes Faculty of Medicine a blended-learning course on environmental health. An e-learning module on PH, lasting 30 to 45 minutes, has been introduced in this course. OBJECTIVE: The objective of this study was to assess the impact of the e-learning module on participants' knowledge and behavior change. METHODS: This was a before-and-after study. The module consisted of 3 parts: introduction, degradation of ecosystems and health (based on the Intergovernmental Panel on Climate Change report and planetary limits), and ecoresponsibility (based on the Shift Project report on the impact of the health care system on the environment). The questionnaire used Likert scales to self-assess 10 points of knowledge and 5 points of PH-related behavior. RESULTS: A total of 95 participants completed the pre- and posttest questionnaires (response rate 55%). The mean scores for participants' pretest knowledge and behaviors were 3.88/5 (SD 0.362) and 3.45/5 (SD 0.705), respectively. There was no statistically significant variation in the results according to age or gender. The pretest mean score of participants who had already taken PH training was statistically better than those who had not taken the PH training before this course (mean 4.05, SD 0.16 vs mean 3.71, SD 0.374; P<.001). CONCLUSIONS: The PH module of the Primary Care Environment and Health course significantly improved self-assessment knowledge scores and positively modified PH behaviors among GP residents. Further work is needed to study whether these self-declared behaviors are translated into practice.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".