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Record W3213792677 · doi:10.3390/ijerph182212073

Peer Support and Overdose Prevention Responses: A Systematic ‘State-of-the-Art’ Review

2021· review· en· W3213792677 on OpenAlexaff
Fiona Mercer, Joanna Miler, Bernie Pauly, Hannah Carver, Kristina Hnízdilová, Rebecca Foster, Tessa Parkes

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2021
Typereview
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsPeer reviewSuicide preventionMEDLINEMedicineInjury preventionHuman factors and ergonomicsPoison controlPsychologyEnvironmental healthPolitical science

Abstract

fetched live from OpenAlex

Overdose prevention for people who use illicit drugs is essential during the current overdose crisis. Peer support is a process whereby individuals with lived or living experience of a particular phenomenon provide support to others by explicitly drawing on these experiences. This review provides a systematic search and evidence synthesis of peer support within overdose prevention interventions for people who use illicit drugs. A systematic search of six databases (CINAHL, SocINDEX, PsycINFO, MEDLINE, Scopus, and Web of Knowledge) was conducted in November 2020 for papers published in English between 2000 and 2020. Following screening and full-text review, 46 papers met criteria and were included in this review. A thematic analysis approach was used to synthesize themes. Important findings include: the value of peers in creating trusted services; the diversity of peers' roles; the implications of barriers on peer-involved overdose prevention interventions; and the stress and trauma experienced by peers. Peers play a pivotal role in overdose prevention interventions for people who use illicit drugs and are essential to the acceptability and feasibility of such services. However, peers face considerable challenges within their roles, including trauma and burnout. Future interventions must consider how to support and strengthen peer roles in overdose settings.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.761
Threshold uncertainty score0.496

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.121
GPT teacher head0.471
Teacher spread0.350 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations83
Published2021
Admission routes1
Has abstractyes

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