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Record W4400953216 · doi:10.21428/cb6ab371.699893cf

“It's going to get pretty tippy”: Stakeholder perspectives on the (dys)function of a four pillars drug strategy

2024· preprint· en· W4400953216 on OpenAlexaboutno aff
Alissa Greer, Naomi Zakimi, Alison Ritter

Bibliographic record

VenueCrimRxiv · 2024
Typepreprint
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsFunction (biology)StakeholderDrugBusinessEngineering ethicsPolitical sciencePharmacologyPublic relationsMedicineEngineeringBiology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.009
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.613
GPT teacher head0.531
Teacher spread0.083 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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