Climate and Health Capacity Building for Health Professionals in Europe: A Pilot Course
Bibliographic record
Abstract
Objectives: The European Climate and Health Responder Course aimed to enhance health professionals' knowledge, confidence, and preparedness to address climate-related health challenges. Methods: The course was delivered as a synchronous, online program targeting health professionals across diverse fields. Data on participant demographics, engagement, and knowledge improvement were collected through pre- and post-course surveys and course completion metrics. Statistical analysis measured changes in participants' confidence and preparedness across targeted outcomes. Results: Of the 4,407 individuals who registered for the course, 21% completed the course, with the majority of them being from Europe and from academic and research institutions. The longitudinal survey revealed significant improvements in participants' self-perceived outcomes across the three target domains from pre-course levels: communication, professional applicability, and self-efficacy. Conclusion: The pilot European Climate and Health Responder course highlights both the strong demand for and the effectiveness of climate change and health education for health professionals. The global interest further highlights the need for expanded climate-health education beyond the European Region.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".