Virtual Opioid Poisoning Education and Naloxone Distribution Programs: A Scoping Review Protocol
Bibliographic record
Abstract
This project is part of the partnership between the Dalla Lana School of Public Health at the University of Toronto and the Canadian Red Cross (CRC) to evaluate the CRC’s Opioid Poisoning Education and Naloxone Distribution program. Funding for the project is provided to the Canadian Red Cross from Health Canada’s Substance Use and Addictions Program. While remote programs already existed prior to the pandemic and were shown to be effective in improving knowledge of opioids and opioid poisoning response, they have not been widely implemented as alternatives to conventional in-person Opioid Poisoning Education and Naloxone Distribution (OPEND) programs. Further, the literature lacks a review on the outcomes and acceptability of remote OPEND programs. In compiling a cohesive overview of existing remote OPEND programs and their advantages and disadvantages, we aim to support the development of future remote OPEND programs and promote them as effective solutions to the various issues of access surrounding traditional, in-person OPEND programs.
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 | 0.093 | 0.082 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.072 | 0.011 |
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".