Legal approaches and government policies enacted to address the overdose epidemic: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this scoping review is to map different past and present legal approaches and government policies that have an intended or unintended effect on the ongoing overdose epidemic. INTRODUCTION: In response to the current overdose epidemic, a number of different legal approaches and government policies have been implemented regarding prescription drugs, illicit substances, and drug use. Additionally, other legal approaches and government policies that do not directly target the overdose crisis (eg, cannabis legalization) may have unintentional effects on opioid use-related harms. The findings of this review will inform policy-makers and individuals working at the forefront of the overdose crisis to help them anticipate the consequences of legal approaches already in place or those that have been recently implemented. INCLUSION CRITERIA: This review will include all legal approaches or government policies that have an intended or unintended effect on the overdose epidemic or on opioid use-related harms or mortality. Only studies published in English from 2000 onward will be included. METHODS: We will search health sciences databases, legal databases, and social sciences databases to ensure comprehensive identification of studies across disciplines. Two independent team members will screen titles and abstracts, and review full-text articles for potential inclusion. One team member will extract data for all studies, and a second team member will verify the data extraction. The results will be presented as a narrative synthesis and in tabular or diagrammatic form.
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 | 0.168 | 0.132 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.010 | 0.013 |
| Bibliometrics | 0.031 | 0.023 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.055 | 0.014 |
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".