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Record W3138716885 · doi:10.1177/1609406921995680

Exploring Peer Support Services for Youth Experiencing Multiple Health and Social Challenges in Canada: A Hybrid Realist-Participatory Evaluation Model

2021· article· en· W3138716885 on OpenAlexafffundabout
Tanya Halsall, Mardi Daley, Lisa D. Hawke, Joanna Henderson

Bibliographic record

VenueInternational Journal of Qualitative Methods · 2021
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
KeywordsMental healthCitizen journalismPeer supportPsychological interventionIntegrated servicesParticipatory action researchPsychologyPublic relationsApplied psychologyComputer scienceSociologyPolitical sciencePsychiatryWorld Wide WebComputer security

Abstract

fetched live from OpenAlex

The Canadian youth services system is fragmented with less than one third of youth accessing the mental health services they need. Experts have called for systems transformation that will increase the integration of youth services and take advantage of complementary services, such as peer supports. Further, researchers have suggested that there is a need to identify the unique contribution and underlying mechanisms that support client recovery within youth peer support interventions. This paper describes the steps taken to implement a hybrid realist and participatory evaluation examining peer support services for youth (14–26 years old) with mental health, physical health and/or substance use challenges. We describe the procedures followed to engage peers in the design of the study and how this was integrated with a realist approach. We also provide a detailed description of the related adaptations to the methods applied within the second stage of the study. Lessons learned through the integration of the two methods are provided as well as potential implications for the findings and related research.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: yes
Qualitativelow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: yes
Other designlow
models splitAgreement compares identical category sets and study designs across arms.

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)0.016
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.185
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.970
GPT teacher head0.699
Teacher spread0.272 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designQualitative · Other design
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

Citations24
Published2021
Admission routes3
Has abstractyes

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