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Record W3154296600 · doi:10.1590/2317-1782/20202020017

Fatores associados às queixas vocais autorreferidas por agentes comunitários de saúde

2021· article· pt· W3154296600 on OpenAlexaff
Júlia de Almeida Nunes Murta, Mariane Silveira Barbosa, Antônio Prates Caldeira, Mirna Rossi Barbosa-Medeiros, Luiza Augusta Rosa Rossi‐Barbosa

Bibliographic record

VenueCoDAS · 2021
Typearticle
Languagept
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsDemographyMedicinePsychologyClinical psychologyGerontology

Abstract

fetched live from OpenAlex

PURPOSE: To verify the prevalence of vocal complaints and their association with sociodemographic, economic, occupational, and behavioral factors among the population of Community Health Agents (CHA). METHODS: This is a cross-sectional and analytical study conducted in the city of Montes Claros, MG, in which 674 CHA participated. Data were collected via a self-administered questionnaire that includes sociodemographic, economic, behavioral, occupational, and voice-use aspects based on the Screening Index for Voice Disorder (SIVD). Bivariate analysis was performed by Pearson's chi-square test and Poisson multiple regression with robust variance to verify the association between the variables. RESULTS: There was a high prevalence of vocal complaints, the most cited being dry throat, throat clearing, tiredness when talking, and hoarseness. We observed a significant association between female gender, lack of restful sleep, alcohol use, regular to very poor self-rated health, and anxiety. CONCLUSION: There was a significant percentage of vocal complaints, and the associated factors found will guide actions to promote vocal and general health.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), 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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.030
GPT teacher head0.308
Teacher spread0.278 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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