Interactive Workshop on Identifying Health Effects of Climate Change in the Clinical Setting
Bibliographic record
Abstract
OBJECTIVE: The aim of the study was to provide clinicians an occupational framework to assess climate-related health conditions, determine at-risk workers, and devise solutions. METHODS: An interactive workshop was presented at the 2022 American Occupational Health Conference. Six climactic events related to occupational health were chosen with corresponding cases from National Institute of Environmental Health Sciences. Participants answered and discussed scripted questions. A 5-point Likert scale utilized by the American Occupational Health Conference evaluated the workshop's quality and utility, and the audience's ability to apply the knowledge. RESULTS: Sixty-one ( N = 66) participants ranked the workshop highly (4.4-4.6/5). Most participants (90%) reported incorporation of practical knowledge gained, increased advocacy capacity, and ability to teach about the issue. CONCLUSIONS: Successful integration of engaging interactive sessions in clinician education on climate change and health is critical as climactic conditions can increase patient vulnerability in their role as workers.
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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 | 0.005 | 0.010 |
| 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 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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".