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Record W4281761438 · doi:10.1007/s10597-022-00978-y

A Psychoeducational Support Group Intervention for People Who Have Attempted Suicide: An Open Trial with Promising Preliminary Findings

2022· article· en· W4281761438 on OpenAlexaff
Myfanwy Maple, Sarah Wayland, Tania Pearce, Rebecca Sanford, Navjot Bhullar

Bibliographic record

VenueCommunity Mental Health Journal · 2022
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsThompson Rivers University
FundersUniversity of New England
KeywordsSuicidal ideationClinical psychologyPsychologySuicide preventionPoison controlMental healthInjury preventionIntervention (counseling)EclipsePsychiatryMedicineMedical emergency

Abstract

fetched live from OpenAlex

Psychoeducational groups have been used to address many health needs. Yet, there are few such options available for people who have attempted suicide. This study presents preliminary findings from an open trial of Eclipse, an 8-week closed, psychoeducational group for people who have attempted suicide. It examined the effectiveness of the Eclipse program in reducing suicidal ideation, depressive symptoms, perceived burdensomeness and thwarted belongingness, and increasing resilience and help-seeking. Results showed statistically significant improvements in depressive symptoms, perceived burdensomeness, resilience and help-seeking from baseline (T1) to immediate post-test (T2), and in perceived burdensomeness from T1 to 1-month follow-up (T3). A pervasiveness analysis showed that over half of the participants reported improvements in key study outcomes, respectively, as a result of participating in the Eclipse group. Psychoeducational support groups could provide broad application for those who have previously attempted suicide in decreasing severity of suicidal thinking by reductions in depressive symptoms, burdensomeness, and thwarted belongingness.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.270
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.147
GPT teacher head0.458
Teacher spread0.311 · 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 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

Citations6
Published2022
Admission routes1
Has abstractyes

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