Climate-sensitive health priorities in Nunatsiavut, Canada
Bibliographic record
Abstract
BACKGROUND: This exploratory study used participatory methods to identify, characterize, and rank climate-sensitive health priorities in Nunatsiavut, Labrador, Canada. METHODS: A mixed method study design was used and involved collecting both qualitative and quantitative data at regional, community, and individual levels. In-depth interviews with regional health representatives were conducted throughout Nunatsiavut (n = 11). In addition, three PhotoVoice workshops were held with Rigolet community members (n = 11), where participants took photos of areas, items, or concepts that expressed how climate change is impacting their health. The workshop groups shared their photographs, discussed the stories and messages behind them, and then grouped photos into re-occurring themes. Two community surveys were administered in Rigolet to capture data on observed climatic and environmental changes in the area, and perceived impacts on health, wellbeing, and lifestyles (n = 187). RESULTS: Climate-sensitive health pathways were described in terms of inter-relationships between environmental and social determinants of Inuit health. The climate-sensitive health priorities for the region included food security, water security, mental health and wellbeing, new hazards and safety concerns, and health services and delivery. CONCLUSIONS: The results highlight several climate-sensitive health priorities that are specific to the Nunatsiavut region, and suggest approaching health research and adaptation planning from an EcoHealth perspective.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".