Construction and Validation of a Tool for the Assessment of the Oral Health in Primary Health Care Through the Perspective of Patients
Bibliographic record
Abstract
OBJECTIVE: To construct and validate a questionnaire to evaluate the quality of oral health services in primary health care, from patients. METHODS: Initially a theoretical model of evaluation of Primary Health Care was elaborated, based on the evaluation of primary care and integrality in primary care. This model served as the basis for the script of a focus group with patients, aiming to verify the attributes perceived as important for such evaluation. The focus group results substantiated the first version of the questionnaire. Content validation was performed through a committee of experts (five teachers/researchers) and face validation in two pre-tests (37 patients each pre-test). For construct validation, factor analysis was performed and reliability (Kappa coefficient) and internal consistency (Cronbach's alpha) were verified. RESULTS: Thirty questions were considered for exploratory factor analysis. The anti-image matrix of covariances showed the need to exclude fourteen questions (values <0.5). After this initial analysis, 16 questions remained in the questionnaire. The KMO test, considering the 16 questions, presented a value of 0.84. Cronbach's alpha was 0.919. The final version contains 16 questions divided into two dimensions: my health unit and the care in my health unit. CONCLUSIONS: The questionnaire allows a strategy that easily evaluates oral health services in primary care, based on the perception of patients.
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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 | 0.049 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".