Patient-planetary health co-benefit prescribing in a circumpolar health region: a qualitative study of physician voices from the Northwest Territories, Canada
Bibliographic record
Abstract
BACKGROUND: Despite climate change being described as a code red for humanity, health systems have been particularly slow in both climate mitigation and adaptation responses. The effects of climate change on health and health systems will not be felt equally, with underserved and marginalised communities disproportionately impacted. The circumpolar region is warming at 3-4 times the global rate, amplifying already existing socioeconomic barriers and health inequities, with particular amplified effects for the substantial Indigenous population in the area. OBJECTIVES AND SETTING: We therefore sought to explore perspectives of physicians around patient-planetary health (P-PH) co-benefit prescribing in a circumpolar region in the Northwest Territories (NWT), Canada, known to be one of the ground zero levels for climate change. METHODS: Thirteen semi-structured physician interviews were carried out in the NWT region between May 2022 and March 2023 using purposive sampling. Interviews were transcribed verbatim and reflexive thematic analysis was carried out to identify key themes. RESULTS: There were three main themes identified including (1) current healthcare system does not support planetary health, (2) supporting patient-planetary health is currently difficult for clinicians and (3) considering change in the NWT to support patient-planetary health. Participants noted key opportunities to move planetary health forward, with the NWT having the potential to be an innovative model for planetary health-informed change for other health systems. CONCLUSION: The NWT health system has unique features due to its rural and remote nature and smaller population base. Despite this, our study identified some key opportunities for advancing P-PH co-benefit efforts. The identified opportunities may be considered in future intervention, organisational change and policy-making efforts with potential relevance in other settings.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.023 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".