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Record W2612837788 · doi:10.1515/bog-2017-0009

Suicide in Turkey: its changes and regional differences

2017· article· en· W2612837788 on OpenAlexaboutno aff
Kadir Temurçin, İsmail KERVANKIRAN

Bibliographic record

VenueBulletin of Geography Socio-economic series · 2017
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSuicide ratesYearbookDemographyQuarter (Canadian coin)Context (archaeology)PopulationGeographySubject matterSuicide preventionPoison controlPolitical scienceMedicineSociologyMedical emergencyLaw

Abstract

fetched live from OpenAlex

Abstract The temporal dimensions and tendencies, including some characteristic features of suicide in Turkey during the social transformation process, are the subject of this study with a focus on the provinces and differentiation on a regional scale. The number of suicides in Turkey and the characteristic features of those committing suicide during the years 1974-2013 have been collected in the ‘Suicide Statistics’ yearbook within this context. Both the suicide numbers as well as the crude suicide rates in Turkey have increased from the last quarter of the 20th century to the beginning of the 21st century. As a matter of fact the number of suicides which was 788 in 1975 increased at a rate of 304.7% to become 3189 in 2013. The crude suicide rate per 100,000 population increased from 1.95 in 1975 to 1.69 in 1980, to 2.42 in 1990, to 2.67 in 2000 and increased to 4.19 in 2013. Although crude suicide rates are smaller than those in most European countries, the fact that there is a rapidly increasing trend indicates that it has started to become a significant public health problem.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.039
GPT teacher head0.289
Teacher spread0.250 · 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

Citations6
Published2017
Admission routes1
Has abstractyes

Explore more

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