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Satisfação dos usuários de cadeiras de rodas da Região Metropolitana da Baixada Santista

2024· article· pt· W4391467348 on OpenAlexaboutno aff
Haidar Tafner Curi, Eliana Chaves Ferretti, Renata Conter Franco, Ana Allegretti, Maria Stella Peccin

Bibliographic record

VenueCiência & Saúde Coletiva · 2024
Typearticle
Languagept
FieldHealth Professions
TopicHealth, Nursing, Elderly Care
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyMedicine

Abstract

fetched live from OpenAlex

This article seeks to identify user satisfaction in relation to wheelchairs and the provision of public and private health services in the Baixada Santista Metropolitan Region. It involved a cross-sectional study with a quantitative approach. Participants answered a sociodemographic questionnaire and the Brazilian version of the Quebec Assistive Technology User Satisfaction Assessment. Data were analyzed using descriptive and comparative statistics by means of Student's t test. Cohen's d effect sizes were also calculated. Participants (n = 42) were "more or less satisfied" with the wheelchairs and "quite satisfied" with the services provided. Rigid frame wheelchair users were significantly more satisfied with their wheelchairs compared to users of wheelchairs weighing over 198 lbs. (p = 0.010, d = 1.04). Users of private services showed significantly greater satisfaction with the provision of the service compared to public services users (p = 0.021, d = 0.75). Wheelchair users in the Baixada Santista Metropolitan Region are more satisfied with the rigid frame wheelchair and less satisfied with public services.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.185

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.062
GPT teacher head0.416
Teacher spread0.354 · 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 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

Citations1
Published2024
Admission routes1
Has abstractyes

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