Harm Reduction and Tensions in Trust and Distrust in a Mental Health Service: A Qualitative Approach
Bibliographic record
Abstract
BACKGROUND: People seeking care for substance use (PSCSU) experience deep social and health inequities. Harm reduction can be a moral imperative to approach these persons. The purpose of this study was to explore relationships among users, health care providers, relatives, and society regarding harm reduction in mental health care, using a trust approach rooted in feminist ethics. METHODS: A qualitative study was conducted in a mental health service for PSCSU, and included fifteen participants who were health care providers, users, and their relatives. Individual in-depth and group interviews, participant observation, and a review of patients' records and service reports were conducted. RESULTS: Three nested levels of (dis)trust were identified: (dis)trust in the treatment, (dis)trust in the user, and self-(dis)trust of the user, revealing the interconnections among different layers of trust. (Dis)trust at each level can amplify or decrease the potential for a positive therapeutic response in users, their relatives' support, and how professionals act and build innovations in care. Distrust was more abundant than trust in participants' reports, revealing the fragility of trust and the focus on abstinence within this setting. CONCLUSION: The mismatch between wants and needs of users and the expectations and requirements of a society and mental health care system based on a logic of "fixing" has contributed to distrust and stigma. Therefore, we recommend policies that increase the investment in harm reduction education and practice that target service providers, PSCSU, and society to change the context of distrust identified.
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 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 | 0.030 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.018 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".