Developing the culture of ethics in population health intervention research in Canada
Bibliographic record
Abstract
Population health intervention research (PHIR) is a particular field of health research that aims to generate knowledge that contributes to the sustainable improvement of population health by enabling the implementation of cross-sectoral solutions adapted to social realities. Despite the ethical issues that necessarily raise its social agenda, the ethics of PHIR is still not very formalized. Unresolved ethical challenges may limit its focus on health equity. This contribution aims to highlight some of these issues and calls on researchers to develop a culture of ethics in PHIR. Three complementary ways are proposed: to build an ethical concept specific to this field, to promote a shared space for critical reflection on PHIR ethics, and to develop the ethical competence in PHIR for which a preliminary framework is proposed.
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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 | 0.167 | 0.131 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.038 | 0.059 |
| Scholarly communication | 0.023 | 0.006 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.001 | 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".