The Health Impacts of Climate Change: A Continuing Medical Education Needs Assessment Framework
Bibliographic record
Abstract
INTRODUCTION: As the health consequences of climate change (CC) will likely become more manifest in the future, family physicians have to be knowledgeable about these impacts and the ways in which they can affect their patients. The main aim of this study was to propose a competency framework and questionnaire used to conduct a needs analysis to identify and prioritize family physicians' real educational needs regarding the health impacts of CC. METHODS: A mixed method combining a qualitative interview and a quantitative online questionnaire was used (n = 24 physicians). The interview assessed key beliefs related to participating in an online continuing medical education (eCME) activity on the health impacts of climate change, and the perception of the key factors or conditions required to ensure the family physicians' satisfaction with this eCME activity. The questionnaire assessed the current and desired levels of competency on five general training themes: general knowledge about CC; heat-related illnesses; CC, extreme weather events and modification of vector-borne and zoonotic diseases; CC, extreme weather events and modification of water-borne diseases; and mental health impacts of natural disasters. RESULTS: Results revealed the need for improved medical education on climate change and health. Results also add to the literature by showing that a 3-hour eCME activity covering these topics would be useful and would allow family physicians to use this knowledge in their daily practice, notably through prevention and counseling. DISCUSSION: Introducing a CME needs assessment framework and a generic instrument that reflects family physicians' needs regarding the health impacts of CC has the added advantage of standardizing the assessment procedure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".