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Record W4386245837 · doi:10.1186/s12954-023-00853-3

“A peer support worker can really be there supporting the youth throughout the whole process”: a qualitative study exploring the role of peer support in providing substance use services to youth

2023· article· en· W4386245837 on OpenAlexafffund
Roxanne Turuba, Ciara Toddington, Miranda Tymoschuk, Anurada Amarasekera, Amanda Madeleine Howard, Violet Brockmann, Corinne Tallon, Sarah Irving, Steve Mathias, Joanna Henderson, Skye Barbic

Bibliographic record

VenueHarm Reduction Journal · 2023
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsOntario Centre of Excellence for Child and Youth Mental HealthUniversity of TorontoCentre for Addiction and Mental HealthCentre for Advancing Health OutcomesSpinal Cord Injury BCUniversity of British ColumbiaProvidence Health Care
FundersHealth CanadaMichael Smith Health Research BC
KeywordsPeer supportMentorshipQualitative researchHealth psychologyPsychologyFocus groupSocial supportPeer groupMental healthParticipatory action researchSocial workMedical educationNursingSocial psychologyMedicinePublic healthPolitical scienceSociologyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Youth (ages 12-24) rarely access services and supports to address substance use concerns. Peer support can facilitate service engagement and has been associated with positive substance use recovery outcomes in adults, yet few studies have examined this role among youth specifically. As such, this qualitative study explored the role of peer support in providing substance use services to youth in British Columbia and how best to support them in their role. METHODS: Participatory action research methods were used by partnering with youth who had lived/living experience of substance use, including peer support workers, to co-design the research protocol and materials. An initial focus group and subsequent interviews were held with 18 peer support workers who provide services to youth (ages 12-24) based on their own lived experience with mental health and/or substance use. The discussions were audio-recorded, transcribed verbatim, and analysed thematically using an inductive approach. RESULTS: Peer support workers' core experiences providing substance use services to youth centred around supporting youth throughout the whole process. This was accomplished by meeting youth where they are at, providing individualized care, and bridging the gap between other services and supports. However, participants experienced multiple organizational barriers hindering their ability to support youth and stressed the importance of having an employer who understands the work you are doing. This involved having someone advocating for the peer support role to promote collaboration, empowering peers to set boundaries and define their own role, and providing adequate training and mentorship. Finally, peer support workers described how their lived experience bridges connection and de-stigmatization at the individual, organizational, and community level, which was unique to their role. CONCLUSIONS: Peer support plays a unique role in youths' substance use journeys, given their own lived experience and flexibility within their role. However, their position is often misunderstood by employers and other service providers, leaving peers with inadequate support, training, and mentorship to do their job. The findings from this study call for improved integration of peer support into service environments, as well as standardized training that is in-depth and continuous.

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)0.018
metaresearch head score (Gemma)0.021
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0150.011
Scholarly communication0.0040.004
Open science0.0020.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.371
GPT teacher head0.485
Teacher spread0.114 · 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

Citations25
Published2023
Admission routes2
Has abstractyes

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