Percepção quanto à qualidade do cuidado de usuários da Rede de Cuidados à Pessoa com Deficiência
Bibliographic record
Abstract
PURPOSE: verify the perception of Care Network users for People with Disabilities in Minas Gerais regarding the quality of care. METHODS: This is an observational, descriptive and cross-sectional study of 871 informants, users and caregivers of Care Network for People with Disabilities in Minas Gerais. Interviews were carried out based on three instruments, the User Interview Roadmap and Brazil's Economic Classification Criteria for sociodemographic, clinical and care characterization, as well as the Quality of Care Scale to assess the quality of care for the interviewees. RESULTS: the majority of users were male, single, unworked and had income of a minimum wage. A higher proportion of elementary school is incomplete, white in color, with household income greater than one and up to two minimum wages. The average age of 98.9% of the users was 28.6 years, of which 50.0% was up to 14 years. The majority reported having a type of disability with no associated comorbidity. Regarding the quality of care referred to, at least a quarter of the interviewees evaluated negatively the Access, Social Needs and Received Information axes. CONCLUSION: It will be necessary to improve the waiting time to receive care and also the services offer where the user resides, to facilitate access to consultations, to contribute intersectorally in leisure and social activities and to improve communication with users to increase the quality of care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".