What competencies physical activity professionals should possess to better integrate climate change related issues into their practice: a Delphi study
Bibliographic record
Abstract
HIGHLIGHTS-Using the Delphi technique, expert consensus identified 18 competencies that physical activity professionals should possess to better integrate climate change related issues into their practice.-Competencies cover 7 knowledge, 8 skills, and 3 attitudes, emphasizing the ability to recognize climate-related health risks, especially for vulnerable populations, adapt exercise prescription, and ensure client safety.-The 18 identified competencies offer a guide for incorporating climate change related issues into physical activity professionals training program. ABSTRACT (245/250 words)Objective: Climate change threatens human health and performance, creating new challenges for physical activity professionals.To prepare them, it is essential to define the competencies required to better integrate climate change-related considerations into their professional practice.This study used the Delphi method to identify such competencies.Methods: A panel of 143 experts was invited to participate in a three-round Delphi study.Round one used an open-ended questionnaire to gather their perspectives, which were analyzed qualitatively to identify recurring themes and sub-themes.In rounds two and three, experts rated their agreement with each competency using 11-point Likert scales.Items with a median score ≥ 8 were retained for Round 3, and those with improved consensus (≥ 70% of scores ≥ 8) and high stability (median variation < 15% between rounds 2 and 3) were retained as consensus competencies.Results: Thirty-four experts from 10 countries reached a final consensus on 18 competencies: 7 knowledge, 8 skills, and 3 attitudes.Within knowledge, strongest agreement concerned recognizing how climate-related health risks disproportionately affect vulnerable populations and identifying risks associated with physical activity during extreme conditions.For skills, consensus was highest for assessing risks linked to outdoor activity and the ability to adapt exercise prescription and make rapid decisions to ensure client safety.For attitudes, the strongest agreement emphasized willingness to adapt professional practice in response to climate extremes.Conclusion: The 18 identified competencies offer a guide for incorporating climate change related issues into physical activity professionals training program.
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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 | 0.038 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".