A tool for assessing continuity of care across care levels: an extended psychometric validation of the CCAENA questionnaire
Bibliographic record
Abstract
BACKGROUND: The CCAENA questionnaire was developed to assess care continuity across levels from the patients' perspective. The aim is to provide additional evidence on the psychometric properties of the scales of this questionnaire. METHODS: Cross-sectional study by means of a survey of a random sample of 1500 patients attended in primary and secondary care in three health care areas of the Catalan health care system. Data were collected in 2010 using the CCAENA questionnaire. To assess psychometric properties, an exploratory factor analysis was performed (construct validity) and the item-rest correlations and Cronbach's alpha were calculated (internal consistency). Spearman correlation coefficients were calculated (multidimensionality) and the ability to discriminate between groups was tested. RESULTS: The factor analysis resulted in 21 items grouped into three factors: patient-primary care provider relationship, patient-secondary care provider relationship and continuity across care levels. Cronbach's alpha indicated good internal consistency (0.97, 0.93, 0.80) and the correlation coefficients indicated that dimensions can be interpreted as separated scales. Scales discriminated patients according to health care area, age and educational level. CONCLUSION: The CCAENA questionnaire has proved to be a valid and reliable tool for measuring patients' perceptions of continuity. Providers and researchers could apply the questionnaire to identify areas for health care improvement.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".