Public health approaches to substance use: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The aim of this scoping review is to describe how a public health approach to legal and illegal psychoactive substance use has been previously defined, and to identify its core values, concepts, activities, and goals. INTRODUCTION: Jurisdictions globally are increasingly endorsing a public health approach to addressing psychoactive substance use. However, there is currently no agreed definition of this approach, and this term has been applied inconsistently in the literature, policy, and practice. A critical first step toward advancing a public health approach to substance use is identifying and articulating its core components. INCLUSION CRITERIA: This review will consider all peer-reviewed and gray literature in English focused on conceptualizing, defining, or describing a public health approach to substance use. Our review does not place limitations on populations, psychoactive substance types, or other contextual factors. METHODS: We will search PROSPERO, MEDLINE, Embase, PsycINFO, Cochrane Library, CINAHL, and Scopus, as well as health and social science databases; websites of prominent nonprofit, civil society, and government agencies/organizations in public health and substance use fields; and reference lists of included articles. Two independent reviewers will screen titles/abstracts of peer-reviewed literature, and 1 reviewer will screen titles/abstracts of gray literature. Two independent reviewers will conduct the full-text screening. A data extraction sheet will be pilot tested through double extraction. Findings will be presented as a narrative summary supported by tables and diagrams and, if feasible, a conceptual framework for understanding and applying a public health approach to substance use. SCOPING REVIEW REGISTRATION NUMBER: Open Science Framework https://osf.io/sv25e.
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 | 0.182 | 0.121 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.023 | 0.020 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.087 | 0.024 |
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".