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Violência física e fatores associados: estudo de base populacional no sul do Brasil

2008· article· pt· W2117177270 on OpenAlexaff
Lílian Palazzo, Alessandra Kelling, Jorge Umberto Béria, Andréia Cristina Leal Figueiredo, Luciana Petrucci Gigante, Beatriz Raymann, Diego G. Bassani

Bibliographic record

VenueRevista de Saúde Pública · 2008
Typearticle
Languagept
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineGynecology

Abstract

fetched live from OpenAlex

OBJECTIVE: To estimate the prevalence of physical violence and its association with sociodemographic aspects, stressful life events, and the use of health services due to emotional problems. METHODS: A cross-sectional population-based study was conducted with a sample of 1,954 14-year-old or older inhabitants of the city of Canoas (Southern Brazil). They were selected by means of conglomerate sampling according to a pre-established system. Data were obtained in visits to households by means of a confidential semi-structured questionnaire. A bivariate analysis was carried out through multinomial logistic regression, and the multivariate analysis by polytomous logistic regression, categorizing the outcome by age group. RESULTS: The findings show a prevalence of 9.7% (CI 95%: 8.37;11.03) and association with: women 20 years old and older (OR=2.74; CI 95%: 1.52;4.94); higher schooling rate (p<0.03); higher experience of stressful life events at 20 years of age or more (OR=6.61; CI 95%: 2.71;16.1); and doctors' appointments due to emotional problems as of 10 years of age (p>0.001). CONCLUSIONS: The prevalence of physical violence in the population was significant, resulting in important emotional consequences and impact on health services, requiring capacity building of the professionals in the field.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.417
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.035
GPT teacher head0.311
Teacher spread0.276 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations10
Published2008
Admission routes1
Has abstractyes

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