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Record W2117733528 · doi:10.1080/07481181003761435

An Evaluation of a Suicide Bereavement Peer Support Program

2010· article· en· W2117733528 on OpenAlexaffabout
Constance A. Barlow, Jeannette Waegemakers Schiff, Urmil Chugh, Dixie Rawlinson, Elizabeth Hides, J. A. Leith

Bibliographic record

VenueDeath Studies · 2010
Typearticle
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsCanadian Mental Health AssociationUniversity of Calgary
Fundersnot available
KeywordsPeer supportMental healthPsychologyIntervention (counseling)Peer groupGriefSuicide preventionNursingAddictionSocial supportPoison controlClinical psychologyMedicinePsychiatryPsychotherapistMedical emergencySocial psychology

Abstract

fetched live from OpenAlex

Peer support, a cornerstone in recovery programs for mental illness and addiction, has not been widely applied to service programs for survivors of suicide. In 2004-2006 Canadian Mental Health Association Suicide Services in Calgary, Alberta, introduced the Peer Support Program for adults, an adjunct to conventional individual and group intervention. This article reports on a mixed-methods evaluation of the Peer Support Program. Hogan's Grief Response Checklist and the qualitative data tracked positive outcomes for both the peer supporters and the clients. This study challenges the unspoken assumption that survivors of suicide, due to their vulnerability, require the services of highly skilled professionals and would not be in a position to offer unsupervised support to peers. Rather it supports an intervention protocol that consists of peer supporters and professionals working collaboratively to offer cost-effective, client-centered services.

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)0.009
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.002
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 head0.164
GPT teacher head0.501
Teacher spread0.337 · 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 designObservational
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

Citations59
Published2010
Admission routes2
Has abstractyes

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