Community of practice: an effective mechanism to strengthen capacity in climate change and health
Bibliographic record
Abstract
Setting Climate change is one of the greatest threats to global health in the twenty-first century and has recently been declared a health emergency.The lack of effective dissemination of emerging evidence on climate change health risks, effects, and innovative interventions to health professionals presents one of the greatest challenges to climate action today.Intervention To identify and address the knowledge gaps at the intersection of health and climate change, the Canadian Coalition for Global Health Research (CCGHR) established a Working Group on Climate Change and Health (WGCCH).WGCCH is evolving organically into a community of practice (CoP) that aims to elevate knowledge brokering on climate change and health and expand to global multi-, inter-, and transdisciplinary realms.Outcomes To date, the WGCCH established a regular webinar series to share expert knowledge from around the world on intersections between climate change and health, developed short summaries on climate change impacts on broad health challenges, supported young professional training, and enhanced climate health research capacity and skills through collegial network development and other collaborative projects that emerged from CoP activities.Implications This paper proposes that WGCCH may serve as an example of an effective strategy to address the lack of opportunities for collaborative engagement and mutual learning between health researchers and practitioners, other disciplines, and the general public.Our experiences and lessons learned provide opportunities to learn from the growing pains and successes of an emerging climate change and health-focused CoP. RésuméLieu Le changement climatique est l'une des plus grandes menaces pour la santé mondiale au 21e siècle et a récemment été déclaré une urgence sanitaire.Le manque de diffusion efficace des données obtenues concernant les risques pour la santé liés au
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.073 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.023 | 0.019 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.008 | 0.066 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.038 | 0.008 |
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".