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Record W2941127260 · doi:10.1111/sltb.12489

Suicide Notes: Assessing Their Impact on the Bereaved

2018· article· en· W2941127260 on OpenAlexaff
William Feigelman, Rebecca Sanford, Julie Cerel

Bibliographic record

VenueSuicide and Life-Threatening Behavior · 2018
Typearticle
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsMental healthSuicide preventionPsychiatryDistressPsychologyEmotional distressPsychological distressMedicineClinical psychologyPoison controlMedical emergencyAnxiety

Abstract

fetched live from OpenAlex

OBJECTIVE: Although suicidologists have devoted great interest toward the importance of suicide notes, scant attention has been paid to their impact upon the suicide bereaved. METHOD: To address this issue we conducted on an online survey querying 146 mostly American suicide bereaved adults who indicated severe emotional distress after their losses, 80% of whom had lost first degree-relatives. RESULTS: We found no significant differences in mental health outcomes between those who received suicide notes and those who had not; nor were differences noted between those whose notes contained helpful or unhelpful information and those who had not received such information. CONCLUSION: We also observed poorer mental health outcomes among the suicide bereaved who expected to receive a suicide note after their loved one died-and did not receive any communication- indicating needs for clinical support among this vulnerable subgroup.

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)0.002
metaresearch head score (Gemma)0.014
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.103
GPT teacher head0.408
Teacher spread0.304 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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