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Record W7014700432

Psychological impacts of engaging in a peer-trainer role in an overdose prevention program

2017· dissertation· en· W7014700432 on OpenAlexaboutno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2017
Typedissertation
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsnot available
Fundersnot available
Keywords(+)-NaloxoneOpioid overdoseHarm reductionHarmMental healthTraining (meteorology)Variety (cybernetics)Drug overdoseSession (web analytics)
DOInot available

Abstract

fetched live from OpenAlex

Background: Peer-led programs have been implemented in a variety of areas, particularly in addiction, mental health, and HIV/AIDS. Research on peer-helpers in these domains has documented numerous psychological benefits associated with the peer-helper role. Specifically, peer-helpers have reported benefits such as increased self-esteem, self-confidence, sense of responsibility, building or improving relationships, having a sense of giving back, hope for the future, and reducing drug use. While these results have been found in the areas of addiction, mental health, and HIV/AIDS, little is known about the peer-helper role in the area of overdose prevention programs. Take-home naloxone programs (THN) represent harm reduction programs with the aim of reducing the number of deaths caused by opioid overdose. Naloxone kits and training on naloxone administration through injection are provided to opioid drug users. Problem: The present problem is that there is a knowledge gap in the literature concerning the benefits and challenges associated with a peer role in overdose prevention research. A THN program, known as PROFAN (prevention and reduction of overdoses - training on, and access to, naloxone) in Montreal has implemented the use of peers training peers, also called peer-trainers. This involves first training drug users in overdose prevention, and giving them the responsibility to then deliver a training session to other users. To our knowledge, this is the first program to allow drug users to train others in overdose prevention. Therefore, the impact of this specific role of being a peer-trainer is unknown. Research Question and Objectives: The main research question guiding this thesis project is: what are the psychological impacts of being a peer-trainer in a THN program? The first manuscript in this thesis represents a literature review on the areas of addiction, overdose prevention, mental health, and HIV/AIDS to explore the experiences of peer-helpers in various domains of peer-led interventions. This review also helped to guide the development of the interview protocol, which was constructed for data collection. The theme of the second manuscript focuses on the personal impacts of being a peer-trainer, reporting on the individual qualitative interview results obtained from the six peer-trainers in the Montreal program. Method: The main objectives of the interviews were to explore peer-trainer experiences of 1) the overdose prevention training that they received, 2) their role as a trainer, and 3) the personal impact of taking on this role. In this thesis, psychological impact is operationalized as empowerment and recovery, as these are two broad themes that have been reported by studies on peer-helpers in the aforementioned areas. Results: Interview results reveal that the six peer-trainers did in fact experience benefits related to empowerment and recovery. As well, there are a number of challenges associated with their role, suggestions to improve the program for future participants, and the desire to be more involved in the program, which suggests that the benefits they gained could be enhanced. The results support findings reported from other domains of peer-led interventions that both the participants (those receiving the training) and peer-trainers are impacted positively by their involvement.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.028
GPT teacher head0.358
Teacher spread0.329 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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

Citations0
Published2017
Admission routes1
Has abstractyes

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