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Record W2016624298 · doi:10.1590/0102-311x00044513

Possibilities and challenges for physical and social environment research in Brazil: a systematic literature review on health behaviors

2013· review· en· W2016624298 on OpenAlexaff
Ana Paula Belon, Candace I. J. Nykiforuk

Bibliographic record

VenueCadernos de Saúde Pública · 2013
Typereview
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSystematic reviewRecreationBuilt environmentPsychological interventionPhysical activityDiversification (marketing strategy)PsychologyEnvironmental healthGerontologyApplied psychologyMedicineMEDLINEPolitical scienceEngineeringBusiness

Abstract

fetched live from OpenAlex

This systematic review analyzed articles focused on the relationship between environment (physical, built, perceived, and social) and smoking, alcohol drinking, physical activity, diet, and obesity in Brazil. Studies published between 19952011 were retrieved from seven databases and hand searches. Based on the 42 articles reviewed, gaps were identified and recommendations were made for future research. Despite a growing number of studies, the Brazilian literature is still limited. The increase of articles in 2010-2011 coincided with the diversification of lifestyles studied, although physical activity domain remains predominant. Most studies analyzed neighborhood settings and used subjective measures for lifestyle and for environment. The presence of recreational facilities was the main physical environment aspect studied, while safety from crime was the prominent social environment factor. More research is needed to yield a rich body of evidence that leads to theoretical and methodological advances, and that supports interventions aimed at creating healthy environments.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.052
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.022
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.052
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.005
Bibliometrics0.0220.023
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.203
GPT teacher head0.462
Teacher spread0.259 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations23
Published2013
Admission routes1
Has abstractyes

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