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Record W4400290757 · doi:10.34119/bjhrv7n3-492

Suicídio na infância: panorama da última década no Brasil

2024· article· pt· W4400290757 on OpenAlexaff
Elaine Angélica Mundim Ribeiro, Júlia Régia Vieira Ramos

Bibliographic record

VenueBrazilian Journal of Health Review · 2024
Typearticle
Languagept
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsNorth Pacific Marine Science Organization
Fundersnot available
KeywordsDemographySociology

Abstract

fetched live from OpenAlex

Os dados de suicídio infantil é um grave índice de mortalidade infantil devido a desdobramentos psiquiátrica, resultante da associação entre causas e fatores de risco diversos. O presente trabalho visa contribuir para uma perspectiva dos óbitos devido ao suicídio nos últimos 10 anos no Brasil. Esse estudo examinou as características e taxas de suicídio na faixa etária de 0-14 anos, com dados do Ministério da Saúde – DATASUS-SIM. O estudo constatou um maior índice de suicídio para o sexo masculino, com diferença significativa para a região Sul do país. As taxas de suicídio foram crescentes na última década para todas as regiões, com destaque para região Centro-Oeste (106,3%). Devido à complexidade do tema, se faz necessário medidas que forneçam acompanhamento e visem minimizar esses índices de óbito.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.157
Threshold uncertainty score0.312

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.007
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.073
GPT teacher head0.410
Teacher spread0.338 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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