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Brazilian normative data for the Short Form 36 questionnaire, version 2

2013· article· en· W2155719587 on OpenAlexaboutno aff
Josué Laguardia, Mônica Rodrigues Campos, Cláudia Travassos, Alberto Lopes Najar, Luiz Antônio dos Anjos, Miguel Murat Vasconcellos

Bibliographic record

VenueRevista Brasileira de Epidemiologia · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsVitalityNormativePsychological interventionQuality of life (healthcare)GerontologyPopulationInequalityDemographySF-36MedicinePsychologyHealth related quality of lifeEnvironmental healthSociologyPolitical sciencePsychiatryDiseaseTheology

Abstract

fetched live from OpenAlex

METHODS: The study Pesquisa Dimensões Sociais das Desigualdades (PDSD) (Social Dimensions of Inequalities) involves 12,423 randomly selected Brazilian men and women aged 18 years old or more from urban and rural areas of the five Brazilian regions, and the information collected included the SF-36 as a measure of health-related quality of life. This provided a unique opportunity to develop age and gender-adjusted normative data for the Brazilian population. RESULTS: Brazilian men scored substantially higher than women on all eight domains and the two summary component scales of the SF-36. Brazilians scored less than their international counterparts on almost all of SF-36 domains and both summary component scales, except on general health status (US), pain (UK) and vitality (Australia, US and Canada). CONCLUSION: The differences in the SF-36 scores between age groups, genders and countries confirm that these Brazilian norms are necessary for comparative purposes. The data will be useful for assessing the health status of the general population and of patient populations, and the effect of interventions on health-related quality of life.

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)0.007
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.357
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.090
GPT teacher head0.400
Teacher spread0.310 · 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 teacher head, not a consensus.

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

Citations142
Published2013
Admission routes1
Has abstractyes

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