Exploring the psychological benefits and challenges experienced by peer-helpers participating in take-home naloxone programmes: A rapid review
Bibliographic record
Abstract
Overdose is a significant problem in many countries around the world and is the leading cause of death among people who use drugs (PWUD). In order to avoid death caused by an overdose, it is necessary to intervene rapidly. Since PWUD often consume together, they are in an optimal position placed to intervene in emergency situations. Some opioid overdose prevention programmes have trained PWUD to intervene by administering naloxone. Little research has been devoted to understanding the benefits associated with the peer-helper role in the context of these programmes. Aim: This rapid review aims to summarise research on the personal impacts of the peer-helper role in overdose prevention programmes, based on peer-helper literature and general findings from peer research from other domains, including mental health and HIV/AIDS programmes. Method: The review includes a search for articles on peer-helpers from PsychInfo, PsychNET, PubMed, PsycARTICLES, Medline, Web of Science, McGill University Library WorldCat network, Scopus, and google scholar. Results: The search generated a total of 152 articles, 27 of which are discussed in detail. Findings: While many articles have been published on the impact of peer-helpers being involved in an intervention, only a few articles have been published on the psychological impacts of being a peer-helper in overdose prevention programmes. Exploratory studies suggest that empowerment and recovery are important benefits. Conditions to optimise these benefits are identified, and are relevant to programme and policy-makers.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".