Cultural Adaptation and Validation of the Portuguese Version of the CANHELP Lite Bereavement Questionnaire
Bibliographic record
Abstract
Background: Satisfaction with care is an important outcome measure in end-of-life care. Validated instruments are necessary to evaluate and disseminate interventions that improve satisfaction with care at the end of life, contributing to improving the quality of care offered at the end of life to the Portuguese population. The purpose of this study was to perform a cross-cultural adaptation and psychometric analysis of the Portuguese version of the CANHELP Lite Bereavement Questionnaire. Methods: Methodological research with an analytical approach that includes translation, semantic, and cultural adaptation. Results: The Portuguese version comprised 24 items. A panel of experts and bereaved family members found it acceptable and that it had face and content validity. A total of 269 caregivers across several care settings in the northern region of Portugal were recruited for further testing. The internal consistency analysis of the adapted instrument resulted in a global alpha value of 0.950. The correlation between the adapted CANHELP questionnaire and a global rating of satisfaction was of 0.886 (p < 0.001). Conclusions: The instrument has good psychometric properties. It was reliable and valid in assessing caregivers’ satisfaction with end-of-life care and can be used in both clinical and research settings.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".