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Record W4309467642 · doi:10.1186/s12913-022-08743-3

“You can kind of just feel the power behind what someone's saying”: a participatory-realist evaluation of peer support for young people coping with complex mental health and substance use challenges

2022· article· en· W4309467642 on OpenAlexafffund
Tanya Halsall, Mardi Daley, Lisa D. Hawke, Joanna Henderson, Kimberly Matheson

Bibliographic record

VenueBMC Health Services Research · 2022
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsCentre for Addiction and Mental HealthUniversity of TorontoCarleton UniversityRoyal Ottawa Mental Health CentreUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsHealth informaticsHealth administrationNursing researchMental healthMedicineCitizen journalismCoping (psychology)Public healthPower (physics)Substance usePeer reviewNursingPsychiatryWorld Wide Web

Abstract

fetched live from OpenAlex

BACKGROUND: Youth peer support, as a practice that aligns youth engagement and participatory approaches, has become increasingly popular in the context of youth mental health services. However, there is a need for more evidence that describes how and why youth peer support practice might be effective. This study was designed to examine a peer support service for youth experiencing complex challenges with mental health, physical health and/or substance use to better understand key features and underlying mechanisms that lead to improved client outcomes. METHODS: We applied a hybrid realist-participatory approach to explore key issues and underlying theoretical assumptions within a youth peer support approach for young people (age 14-26) experiencing complex mental health and substance use challenges. We used semi-structured interviews and focus groups with staff, including peers (N = 8), clinical service providers and administrative staff (N = 15), to develop the theories and a client survey to validate them. Our qualitative thematic analysis applied a retroductive approach that involved both inductive and deductive processes. For the client survey (N = 77), we calculated descriptive statistics to examine participant profiles and usage patterns. Pearson correlations were examined to determine relationships among concepts outlined in the program theories, including context, mechanism and outcome variables. RESULTS: Our analyses resulted in one over-arching context, one over-arching outcome and four program theories. Program theories were focused on mechanisms related to 1) positive identity development through identification with peers, 2) enhanced social connections, 3) observational learning and 4) enhanced autonomy and empowerment. CONCLUSIONS: This study serves as a unique example of a participatory-realist hybrid approach. Findings highlight possible key components of youth peer practice and shed light on the functional mechanisms that underlie successful peer practice. These key components can be examined in other settings to develop more comprehensive theories of change with respect to youth peer support and can eventually be used to develop guidelines and standards to strengthen practice. This research contributes to an expanding body of literature on youth peer support in mental health and connects peer practice with several social theories. This research begins to lay a foundation for enhanced youth peer support program design and improved outcomes for young people experiencing complex mental health and substance use challenges.

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)0.060
metaresearch head score (Gemma)0.061
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 score0.060
Threshold uncertainty score0.315

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0600.061
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0060.006
Scholarly communication0.0030.003
Open science0.0030.008
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.612
GPT teacher head0.546
Teacher spread0.066 · 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

Citations37
Published2022
Admission routes2
Has abstractyes

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