“The health equity curse”: ethical tensions in promoting health equity
Bibliographic record
Abstract
BACKGROUND: Public health (PH) practitioners have a strong moral commitment to health equity and social justice. However, PH values often do not align with health systems values, making it challenging for PH practitioners to promote health equity. In spite of a growing range of PH ethics frameworks and theories, little is known about ethical concerns related to promotion of health equity in PH practice. The purpose of this paper is to examine the ethical concerns of PH practitioners in promoting health equity in the context of mental health promotion and prevention of harms of substance use. METHODS: As part of a broader program of public health systems and services research, we interviewed 32 PH practitioners. RESULTS: Using constant comparative analysis, we identified four systemic ethical tensions: [1] biomedical versus social determinants of health agenda; [2] systems driven agendas versus situational care; [3] stigma and discrimination versus respect for persons; and [4] trust and autonomy versus surveillance and social control. CONCLUSIONS: Naming these tensions provides insights into the daily ethical challenges of PH practitioners and an opportunity to reflect on the relevance of PH frameworks. These findings highlight the value of relational ethics as a promising approach for developing ethical frameworks for PH practice.
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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 | 0.154 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.144 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.012 | 0.018 |
| 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".