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Record W2936477518 · doi:10.1192/bjb.2019.26

Effects of patient suicide on psychiatrists: survey of experiences and support required

2019· article· en· W2936477518 on OpenAlexaboutno aff
Rachel Gibbons, Fiona Brand, Anne Carbonnier, Alison Croft, Karen Lascelles, Gislene Wolfart, Keith Hawton

Bibliographic record

VenueBJPsych Bulletin · 2019
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)MedicineEmotional supportClinical PracticeSuicide preventionPsychiatryFamily medicineSocial supportPsychologyPoison controlMedical emergencySocial psychology

Abstract

fetched live from OpenAlex

Aims and method Death of patients by suicide can have powerful effects on psychiatrists. We report the findings of a survey completed by 174 psychiatrists on the effects of patient suicide on their emotional well-being and clinical practice, and the support and resources they felt would be helpful. Results and clinical implications The death of a patient by suicide usually had a major effect on respondents. Clinical practice was often negatively affected, and over a quarter of respondents considered a change of career path as a result. There were some gender differences in responses, with women reporting more sense of responsibility for the deaths and a greater effect on their clinical confidence. Desired support included a senior suicide lead clinician, support during formal post-suicide processes, opportunity for reflection on practice, information about resources to support families and help communicating with families and friends of the deceased.

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)0.017
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.020
GPT teacher head0.299
Teacher spread0.280 · 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

Citations100
Published2019
Admission routes1
Has abstractyes

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