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Record W2008900288 · doi:10.3109/09638237.2013.799265

Qualitative research on suicide in East Asia: A scoping review

2013· review· en· W2008900288 on OpenAlexaff
Christina Han, John S. Ogrodniczuk, John L. Oliffe

Bibliographic record

VenueJournal of Mental Health · 2013
Typereview
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEast AsiaQualitative researchGeographyPolitical scienceChinaSociologySocial scienceArchaeology

Abstract

fetched live from OpenAlex

BACKGROUND: Suicide is a serious problem in East Asia. Yet, there is a significant lack of research on the topic, particularly using qualitative methodology. OBJECTIVES: This scoping review reports on findings drawn from 11 qualitative studies, providing up-to-date knowledge and understandings about suicide in East Asian populations. METHODS: A web-based literature search was performed to identify empirical qualitative research articles addressing suicide in East Asia, published from January 2002 to December 2011. RESULTS: Three themes were identified within the reviewed studies; (1) influence of cultural beliefs; (2) the role of caregivers; and (3) specific sociological contexts. These themes are interrelated rather than mutually exclusive. CONCLUSION: The findings drawn from this scoping review reveal specific as well as broad contexts where suicidal ideation and behaviours occur among East Asians. To advance understandings, future studies should focus on comparative and longitudinal research to distil prevailing trends as well as the specificities that reside among particular East Asian subgroups (i.e. gender, sexual identity and generational) as a means to developing culturally sensitive and targeted suicide prevention programs.

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)0.043
metaresearch head score (Gemma)0.090
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.043
Threshold uncertainty score0.226

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0430.090
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0180.019
Science and technology studies0.0030.003
Scholarly communication0.0050.006
Open science0.0020.005
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.600
GPT teacher head0.673
Teacher spread0.072 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations14
Published2013
Admission routes1
Has abstractyes

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