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Record W2935160817 · doi:10.1590/interface.170803

Narrativas de sofrimento emocional na Atenção Primária: contribuições para uma abordagem integral culturalmente sensível em Saúde Mental Global

2019· article· pt· W2935160817 on OpenAlexfundaboutno aff
Alice Lopes do Amaral Menezes, Karen Athié, César Augusto Orazem Favoreto, Francisco Ortega, Sandra Fortes

Bibliographic record

VenueInterface - Comunicação Saúde Educação · 2019
Typearticle
Languagept
FieldHealth Professions
TopicHealth, Nursing, Elderly Care
Canadian institutionsnot available
FundersMcGill University
KeywordsPsychologyNarrativeFeelingMedicineSocial psychologyPhilosophy

Abstract

fetched live from OpenAlex

Autores da Saúde Mental Global defendem, de um lado, o aumento do acesso aos cuidados de saúde mental, inclusive com o uso de intervenções padronizadas. De outro lado, na Atenção Primária à Saúde no Brasil, a apresentação sintomatológica do sofrimento emocional pelos pacientes dificulta sua identificação pelos profissionais, comprometendo o cuidado e exigindo uma abordagem culturalmente sensível. Objetivando conhecer a percepção de pacientes sobre o sofrimento emocional e seu cuidado na Atenção Primária, adotou-se método qualitativo de abordagem narrativa, com coleta de dados em grupos em salas de espera e roteiro baseado no instrumento McGill Ilness Narrative Interview. As narrativas foram tratadas por Análise de Conteúdo e revelaram que limitar a comunicação do sofrimento é negar o acesso ao cuidado. Contrapondo tal limitação, apontaram-se estratégias para nortear a estruturação de um cuidado que seja integral e culturalmente sensível.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0030.002
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0090.008

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 head0.057
GPT teacher head0.435
Teacher spread0.378 · 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 designQualitative
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
Published2019
Admission routes2
Has abstractyes

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