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Record W2121062420 · doi:10.3109/09638237.2015.1057322

Authentic peer support work: challenges and opportunities for an evolving occupation

2015· article· en· W2121062420 on OpenAlexaffabout
Karen Rebeiro Gruhl, Sara Lacarte, Shana Calixte

Bibliographic record

VenueJournal of Mental Health · 2015
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsNOSM UniversityLaurentian University
Fundersnot available
KeywordsWork (physics)PsychologyEngineering

Abstract

fetched live from OpenAlex

BACKGROUND: The peer support worker (PSW) belongs to the fastest growing occupation in the mental health sector, yet it is often under-valued and poorly understood. Despite an emerging evidence base, and strong support from mental health service users, the PSW remains on the periphery of mainstream services in northeastern Ontario. AIMS: To examine the role of the PSW, along with the challenges and benefits, and to understand why the PSW is not more integrated within mainstream services. METHODS: A sequential, exploratory, mixed-methods design was used to collect data on 52 survey and 33 focus group participants. Qualitative data were analyzed thematically. RESULTS: Peer support work was described by participants as being authentic when PSWs can draw upon lived experience, engage in mutually beneficial discussions, and be a role model. Authentic peer support was noted to be important to the recovery of mental health service users; yet, participants revealed that many positions continue to reflect more generic duties. Challenges to further integration include acceptance, training and credentialing, self-care, and voluntarism. CONCLUSIONS: Future development and mainstream integration of peer support work must reconcile current tensions between standardization and loss of authenticity. Training in communicating the lived experience, setting boundaries and self-care are important steps forward.

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 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)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.435
Threshold uncertainty score0.484

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.633
GPT teacher head0.501
Teacher spread0.132 · 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 teacher head, 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

Citations127
Published2015
Admission routes2
Has abstractyes

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