Just have this come from their prescription pad: the medicalization of safer supply from the perspectives of health planners in BC, Canada
Bibliographic record
Abstract
Context In March 2020, British Columbia introduced the Risk Mitigation Guidance (RMG) to enable the prescription of pharmaceutical substitutes for the unregulated drug market to decrease overdose deaths and COVID-19 infections. This study presents health planners’ perspectives on the RMG adoption within a medicalized system of care.Methods We conducted interviews with 28 health planners to obtain their views on implementation. We undertook a thematic analysis, drawing on the theory of medicalization to analyze and interpret findings.Findings We identified four themes regarding the implementation of the RMG within a medical model: (1) The medical model as expeditious and pragmatic; (2) Increasing medicalization of safer supply in response to prescriber tensions and distress; (3) Intersecting harms to people who use drugs; (4) Recommendations for additional safer supply models, decriminalization and regulation.Conclusions Health planners recognized and often problematized the over-medicalized nature of current safer supply models. Challenges with medicalization include privileging dominant ideologies (e.g. biomedicine), exerting social control, and perpetuating inequities. Greater attention to the relationships between harm reduction, medical, and criminal justice models in drug policy is needed to avoid compromises of medicalization within criminalization. Grassroots harm reduction may be critical in advancing safer supply beyond medical models.
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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.042 | 0.023 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".