What is a public health approach to substance use? A qualitative systematic review and thematic synthesis
Bibliographic record
Abstract
BACKGROUND: Psychoactive substance use and the regulations that govern it both have the potential to lead to harm. A 'public health approach' (PHA) is frequently invoked as a means of addressing these harms, but the term is used in inconsistent and contradictory ways. This study systematically reviewed the English-language academic literature to understand how a public health approach to substance use is defined and described. METHODS: This review employed thematic synthesis, a methodology designed to rigorously synthesize qualitative evidence. Eligible articles were published in peer-reviewed journals, in the English language, with full text available, and focused primarily on substance use. There were no limits on year of publication. Original research, opinion/commentary, and reviews were included. The searches were conducted in October 2021 in CINAHL, Embase, Medline, PAIS Index, PsycINFO, Scopus, Sociological Abstracts, and Web of Science. RESULTS: 272 articles from 25 countries, published between 1950 and 2021, were synthesized. Definitions of a PHA have changed over time and differ by substance. The most commonly cited characteristics of a PHA were: for alcohol, regulation, e.g. of price and availability (54% of articles); for cannabis: regulation (68%); for illicit drugs: that a PHA is distinct from a criminal justice approach (63%); for opioids: substance use disorder treatment (55%); and for tobacco: regulation (62%). CONCLUSION: There is no consensus on the definition of a public health approach to substance use, but there is substantial agreement when it comes to PHAs to specific substances. There are also similarities in how they are described for legal substances versus illicit ones. This review found areas of disagreement regarding the extent to which PHAs should focus on individual-level factors. Policymakers, academics, and others developing or implementing PHAs to substance use should be explicit about their aims and objectives - as well as the premises and assumptions underlying them.
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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 | 0.147 | 0.204 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.021 | 0.024 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".