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Record W4391420206 · doi:10.33137/jrmh.v7i1.41620

Integrating Peer Support Workers into Mental Health Programs

2024· article· en· W4391420206 on OpenAlexafffundabout
Samantha Sexsmith Chadwick, Heather Fahr, Jytte Maleski

Bibliographic record

VenueJournal of Recovery in Mental Health · 2024
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsCanadian Mental Health Association
FundersCanadian Mental Health Association
KeywordsMental healthInclusion (mineral)Peer supportIntervention (counseling)PsychologyProgram evaluationMedical educationAction researchPlan (archaeology)Peer educationMental illnessAction planNursingHealth educationMedicinePublic healthPsychiatryPolitical scienceGeographyPedagogyManagementSocial psychology

Abstract

fetched live from OpenAlex

Objective: Canadian Mental Health Association - Calgary Region (CMHA Calgary) works to reduce the impact of mental illness and addiction in the community. This is actioned through mental health programming focused on education, prevention, and early intervention. The organization places high value on the inclusion of lived experience in mental health programming in the form of Peer Support (PS). CMHA Calgary’s five-year strategic plan included a goal to formally integrate Peer Support Workers (PSWs) into all established programs with thought and intention. Research Design and Methods: CMHA Calgary integrated peers into mental health programs using a collaborative and developmental approach. The project team developed an evaluation framework as a guide to collect feedback and understand the impact of the pilot initiative. Results: This approach allowed for real-time responses and data collection, which lead to rapid action to improve the approach before it was spread to other programs. The project allowed for the development of program materials for future application. Conclusions: This project provided CMHA Calgary with tangible, actionable information on how to integrate PSWs into all of their programs.

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)0.009
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.560
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.125
GPT teacher head0.465
Teacher spread0.340 · 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.

Study designNot applicable
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

Citations0
Published2024
Admission routes3
Has abstractyes

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