“It's going to get pretty tippy”: Stakeholder perspectives on the (dys)function of a four pillars drug strategy
Bibliographic record
Abstract
A ‘drug strategy’ is a policy document that structures the priorities and directions for interventions for drug related issues within a particular jurisdiction and/or context. A ‘pillars’ drug strategy concentrates efforts through clustering separated columns of activity, such as law enforcement, harm reduction, treatment, and prevention. In this study, we examined drug policy stakeholders’ perspectives on the structure, function, and fit of a four pillar drug strategy framework in Vancouver, Canada. Utilizing qualitative interview data from fifteen drug policy stakeholders, we examine perspectives on Vancouver's four pillar drug strategy that was implemented over 20 years ago. Our findings are organized under three main themes: (1) the notion of ‘balance’ of efforts, resources, and attention across the pillars; (2) how the pillars function as a cohesive whole; (3) whether the pillars’ architecture is still fit-for-purpose. The architecture of four discrete pillars did not enable a sense of cohesion and collaboration of efforts, and instead elicited a sense of competition, conflict, fragmentation, simplicity, and rigidity of the strategy as a whole. These findings suggest that, in practice, a four pillars framework may be structurally dysfunctional in working towards a common goal. Our study questions the effectiveness of a commonly used 'pillars' framework and whether it needs to be reenvisaged.
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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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.033 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".