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Record W4213166818 · doi:10.1177/13634615221076424

Suicide in cultural context: An ecosocial approach

2022· editorial· en· W4213166818 on OpenAlexaff
Laurence J. Kirmayer

Bibliographic record

VenueTranscultural Psychiatry · 2022
Typeeditorial
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsTypologyContext (archaeology)Embodied cognitionPoison controlSocial environmentThematic analysisSuicide preventionPsychologyMental healthSociologyDevelopmental psychologySocial psychologyQualitative researchPsychiatryMedicineSocial scienceAnthropologyEpistemologyGeography

Abstract

fetched live from OpenAlex

on suicide in cultural context. Developmental and social structural factors including exposure to violence, childhood abuse and privation, as well as intractable social problems that create psychic pain and a sense of entrapment have been shown to increase the risk of suicidal behavior. However, all of the major social determinants identified in suicide research are influenced or mediated by particular cultural meanings and contexts. To move beyond crude generalizations about suicide based on psychological theories developed mainly in Western contexts and culture-specific prototypes or exemplars, we need more fine-grained analysis of the experience of diverse populations. The articles in this issue provide clear illustrations of the impact of cultural and contextual factors in the causes of suicide, with implications for psychiatric research, theory, and practice. Cross-cultural research points to the possibility of developing a typology of social predicaments affecting specific sociodemographic groups and populations. This typology could be elaborated and applied in clinical and public health practice through an ecosocial approach that considers the ways that suicide is embodied and enacted in social systemic contexts.

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)0.006
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.015
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.018
Meta-epidemiology (narrow)0.0050.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0050.003
Science and technology studies0.0050.006
Scholarly communication0.0100.007
Open science0.0040.003
Research integrity0.0150.028
Insufficient payload (model declined to judge)0.0030.002

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 head0.034
GPT teacher head0.341
Teacher spread0.307 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations50
Published2022
Admission routes1
Has abstractyes

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