ETHICAL ANALYSIS IN PUBLIC HEALTH PRACTICE: A MULTI-SECTORAL MIXED-METHODS STUDY
Bibliographic record
Abstract
Rationale: All decisions in public health practice involve implicit value judgements, and many involve explicit reference to ethical principles. Despite the increased awareness, interest and literature in Public Health Ethics in the past decade, there remains little understanding of what public health practitioners or trainees mean by ethics, what meta-ethical foundations shape their approach to ethical dilemmas, and what prior training in ethics they have had or wish to have. This study aims to answer some of these questions and will serve as the basis for the development of resources to aid public health decision-making. Method: Qualitative and quantitative data were collected from public health practitioners by means of paper and web-based surveys, as well as structured interviews. Data was coded and analysed using SPSS 15. Results: 16/20 trainees, 70/150 Canadian practitioners, and 508/2058 American practitioners responded to the survey; 10 interviews were conducted. There was remarkable heterogeneity of responses regarding prioritization of values and meta-ethical justification of ethical norms. Respondents reported little training in ethics and considerable in enhancing their skills. Conflict between ethical imperatives and the law were a prominent feature of American, but not Canadian respondents. Conclusions: Public Health practitioners hold a variety of disparate views regarding ethics in public health. These translate into different understandings of the goals and means of public health, with far reaching implications in all spheres of practice. A Public Health Ethical Reflection tool was developed to enhance ethical awareness in goal setting, planning and implementation of public health interventions.
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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 | 0.092 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".