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Record W2581069024 · doi:10.47741/17943108.110

Suicidio en las cárceles de Chile durante la década 2006-2015

2016· article· es· W2581069024 on OpenAlexaff
Francisco Ceballos-Espinoza, Ana‐María Chávez‐Hernández, Gustavo-Morelos Padilla-Gallegos, Antoon A. Leenaars

Bibliographic record

VenueDialnet (Universidad de la Rioja) · 2016
Typearticle
Languagees
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsWindsor Clinical Research
Fundersnot available
KeywordsHumanitiesGeographyPhilosophy

Abstract

fetched live from OpenAlex

Chile apresenta um aumento alarmante de suicides, tanto na população geral quanto penitenciária, que preocupa à s autoridades da justiça e a saúde. Objetivo: analisar os suicídios consumados por prisioneiros em cadeias chilenas durante os anos 2006-2015, para obter o perfil de características sociais e criminó genas do ato suicida e dos centros penitenciários. Metodologia: de um total de 162 suicidos, 132 dos casos examinados pela Polícia das Pesquisas do Chile foram analisados. Resultados: 97.7% dos suicidos aconteceram nos homens de todas as idades (de 16 a 74 anos); duas terceiras partes (66.7%) aconteceram em pessoas com renda prévia à prisão, embora a maioria não tivesse os registros criminalis nem as sentenças precedentes (97.7%). Uma maioria (65.1%) aconteceu durante o primeiro ano da entrada. 73.5% eram solteiros, 47% só tinham estudos básicos, 84.8% não possuíam um ofício estável. Em 43.2% o estado depressivo foi relatado como ativador do suicido, e o método o mais comum foi o enforcamento (97%). As diferenças com estatísticas significativas de determinadas correlações foram encontradas, como entre o nível de estudos e a razão para o suicido, e entre o nível de estudos e renda prévia à prisão; também, uma correlação relevante que amostra que à idade menor, mais renda à prisão e menor nível de estudos

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 score0.246
Threshold uncertainty score0.489

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.281
Teacher spread0.270 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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