Reimagining health services and policy research: Embedding environmental sustainability into our research priorities and practice
Bibliographic record
Abstract
The health impacts of the climate crisis demand that health systems adapt their practices and mitigate their carbon emissions. Health services and policy research (HSPR) is crucial for the transformation of these systems. We report on an initiative by HSPR trainees from across Canada to ideate on health systems transformation towards environmental sustainability. We outline how environmental sustainability must be embedded into HSPR. We suggest that this process must include a justice-based approach and that HSPR curricula, conduct, and content must consider the health of people and the planet. Furthermore, these endeavours must be supported by funding opportunities and granting organizations. We then offer ways forward for trainees, and those who support them, to ensure environmental sustainability is embedded within HSPR.
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 | 0.374 | 0.246 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.021 | 0.103 |
| Scholarly communication | 0.043 | 0.027 |
| Open science | 0.008 | 0.045 |
| Research integrity | 0.019 | 0.032 |
| Insufficient payload (model declined to judge) | 0.005 | 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".