Short-form quality care questionnaire-palliative care has acceptable measurement properties in Brazilian cancer patients
Bibliographic record
Abstract
BACKGROUND: Our objective was to perform the translation, cross-cultural adaptation, and validation of the Quality Care Questionnaire-Palliative Care (QCQ-PC) into Brazilian Portuguese for cancer patients in palliative care. The translation and cross-cultural adaptation comprised the following stages: translation, synthesis of translations, back-translation, analysis by a committee of experts, testing of the pre-final version, and definition of the final version. The evaluated measurement properties were: structural validity using factor analysis, test-retest reliability using the intraclass correlation coefficient (ICC), internal consistency using Cronbach's alpha, and construct validity using the correlations between the QCQ-PC and other questionnaires already validated in Brazil. RESULTS: Two hundred and twenty-five cancer patients were included for validity analyses, and a subsample of 30 patients was used for test-retest reliability. The most adequate fit indexes were for the short version of the QCQ-PC (SF-QCQ-PC), with two domains and 12 items. There was adequate reliability and internal consistency, with values of the ICC ≥ 0.83 and Cronbach's alpha ≥0.82. There were correlations > 0.30 between the SF-QCQ-PC and the Karnofsky Performance Scale, the Palliative Prognostic Index, the sadness domain of the Edmonton Symptom Assessment System, the Barthel Index, and all domains related to the McGill Quality of Life Questionnaire and the European Organization for Research in the Treatment of Cancer Questionnaire-core. CONCLUSION: The short version of the SF-QCQ-PC has acceptable psychometric properties for use in Brazil.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".