Navigating conflicting value systems: a grounded theory of the process of public health equity work in the context of mental health promotion and prevention of harms of substance use
Bibliographic record
Abstract
BACKGROUND: Promoting health equity and reducing heath inequities is a foundational aim and ethical imperative in public health. There has been limited attention to and research on the ethical issues inherent in promoting health equity and reducing health inequities that public health practitioners experience in their work. The aim of the study was to explore how public health providers identified and navigated ethical issues and their management related to promoting health equity within services focused on mental health promotion and preventing harms of substance use. METHODS: Semi-structured individual interviews and focus groups were conducted with 32 public health practitioners who provided public-health oriented services related to mental health promotion and prevention of substance use harms (e.g. harm reduction) in one Canadian province. RESULTS: Participants engaged in the basic social process of navigating conflicting value systems. In this process, they came to recognize a range of ethically challenging situations related to health equity within a system that held values in conflict with health equity. The extent to which practitioners recognized, made sense of, and acted on these fundamental challenges was dependent on the degree to which they had developed a critical public health consciousness. Ethically challenging situations had impacts for practitioners, most importantly, the experiences of responding emotionally to ethical issues and the experience of living in dissonance when working to navigate ethical issues related to promoting health equity in their practice within a health system based in biomedical values. CONCLUSIONS: There is an immediate need for practice-oriented tools for recognizing ethical dilemmas and supporting ethical decision making related to health equity in public health practice in the context of mental health promotion and prevention of harms of substance use. An increased focus on understanding public health ethical issues and working collaboratively and reflexively to address the complexity of equity work has the potential to strengthen equity strategies and improve population health.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.046 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".