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Record W4413233643 · doi:10.1080/23303131.2025.2544678

People with Lived Experience as Support Workers and Community Leaders: Findings from a Critical Interpretive Synthesis of the Literature and Expert Consultation

2025· article· en· W4413233643 on OpenAlexaff
Sarah Tremblett, Stephen Ellenbogen, Emily Wadden, Lily Koparan, Christina Traverse, Melendy Muise

Bibliographic record

VenueHuman Services Organizations Management Leadership & Governance · 2025
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsStatistics CanadaSafe Engineering Services & Technologies (Canada)Government of CanadaMemorial University of NewfoundlandWilfrid Laurier University
Fundersnot available
KeywordsLived experiencePsychologySociologyMedical educationMedicinePsychotherapist

Abstract

fetched live from OpenAlex

In this critical interpretive synthesis, we explore the roles, benefits, and challenges of peer support workers with lived experience (PSWLE) who work with marginalized populations, integrating research findings and knowledge from key informants. We find that PSWLE are engaged in interconnected practice, consultative, and educative roles and serve as intermediaries between public services and marginalized populations; these roles vary according to field of practice; PSWLE perform work that benefits service users, human service organizations, and the public; and PSWLE are encumbered by various workplace challenges, like ambiguous job descriptions and transition from service user to provider. PSWLE and other health and social service providers can experience tensions due to differences in practice models (e.g. harm reduction vs. medical model). Governments and human service organizations need to modify organizational and service delivery systems to ensure that PSWLE are adequately supported and capable of practicing according to peer support principles.

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)0.093
metaresearch head score (Gemma)0.144
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 score0.093
Threshold uncertainty score0.494

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0930.144
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0140.010
Science and technology studies0.0130.022
Scholarly communication0.0160.012
Open science0.0030.012
Research integrity0.0040.006
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 head0.078
GPT teacher head0.370
Teacher spread0.292 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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