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Reformulando a violência política e efeitos na saúde mental: esboçando uma agenda de pesquisa e ação para a América Latina e região do Caribe

2006· article· pt· W1999931667 on OpenAlexaff
Duncan Pedersen

Bibliographic record

VenueCiência & Saúde Coletiva · 2006
Typearticle
Languagept
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsMcGill University
Fundersnot available
KeywordsSociology

Abstract

fetched live from OpenAlex

Em décadas recentes, o número de pessoas expostas a eventos traumáticos tem aumentado significativamente, bem como formas de violência como guerras e revoluções políticas, que subjugam populações civis em todo o mundo. Apesar da disseminação dos conflitos armados, guerrilhas e violência política na América Latina e Caribe, atenção insuficiente tem sido dada para avaliar o impacto psicológico a médio e longo prazo e o peso das doenças, mortes, e invalidez provocadas pela violência e guerra contra populações civis. Algumas perguntas centrais são levantadas, a partir de revisão da literatura: qual o impacto na saúde da população, a curto, médio e longo prazo, por vivenciar violências extremas e continuadas? Como a violência política se relaciona com pobre saúde mental individual e coletiva? As desordens relacionadas aos traumas são conseqüências universais da violência extrema e continuada? Essas perguntas nos levam a reformular a análise da violência política e de suas conseqüências sobre a saúde mental e a reexaminar as noções de trauma e a agenda da pesquisa e ação para a região. Ao fim, são apresentados alguns princípios básicos que podem ser úteis ao se projetar intervenções psicosociais.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.181
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.003

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 head0.029
GPT teacher head0.359
Teacher spread0.329 · 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

Citations7
Published2006
Admission routes1
Has abstractyes

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