Equity in public health ethics: a community-engaged, empirical study of values, principles, and practices
Bibliographic record
Abstract
BACKGROUND: Public health ethics provides a framework for navigating ethical dilemmas, distinguished from clinical bioethics by its focus on population health, prevention, and explicit acknowledgement of political context. While public health ethical frameworks have been developed, concerns are raised about lack of utility or integration of equity and Indigenous perspectives (First Nations, Inuit, Métis). This study aimed to engage groups impacted by public health decisions to identify ethical values and develop recommendations for public health ethics practice in British Columbia. METHODS: A two-phase, community-engaged qualitative study was conducted from March 2024 to January 2025. 40 public health professionals and members of equity-deserving groups (people with disabilities, Indigenous Peoples, newcomer, seniors), were recruited for interviews or focus groups in phase one. Phase two involved follow-up surveys or interviews with 18 participants. Directed content analysis was utilized to identify themes. RESULTS: Participants emphasized the importance of equity, humanization, Indigenous Cultural Safety, and wholistic wellbeing, as well as relational values of trust and transparency. Recommendations included increasing equity through addressing social determinants of health and developing accessible practice tools. CONCLUSIONS: Engaging equity-deserving groups generated insights and actionable recommendations to improve equity, strengthen public health ethics practice and build trust in public health.
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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 | 0.041 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.027 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".