The development and validation of a novel questionnaire to measure patient and family satisfaction with end-of-life care: the Canadian Health Care Evaluation Project (CANHELP) Questionnaire
Bibliographic record
Abstract
The purpose of this study was to further validate a novel instrument to measure satisfaction with end-of-life care, called the Canadian Health Care Evaluation Project (CANHELP) questionnaire. Data were collected by a cross-sectional survey of patients who had advanced, life-limiting illnesses and their family caregivers, and who completed CANHELP, a global rating of satisfaction, and a quality of life questionnaire. We conducted factor analysis, assessed internal consistency using Cronbach's alpha, and evaluated construct validity by describing the correlation amongst CANHELP, global rating of satisfaction and the quality of life questionnaire scores. There were 361 patient and 193 family questionnaires available for analysis. In the factor analysis, we identified six easily interpretable factors which explained 55.4% and 60.2% of the variance for the patient and caregiver questionnaire, respectively. For the patient version, the subscales derived from these factors were Relationship with Doctors, Illness Management, Communication, Decision-Making, Role of the Family, and Your Well-being. For the family questionnaire, the factors were Relationship with Doctors, Characteristics of Doctors and Nurses, Illness Management, Communication and Decision-Making, Your Involvement, and Your Well-being. Each subscale for each questionnaire had acceptable to excellent internal consistency (Cronbach's alpha ranged from 0.69-0.94). We observed good correlations between the CANHELP overall satisfaction score and global rating of satisfaction (correlation coefficient 0.49 and 0.63 for patient and family, respectively) which was greater than the correlations between CANHELP and the quality of life instruments. We conclude that the CANHELP Questionnaire is a valid and internally consistent instrument to measure satisfaction with end-of-life 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 | 0.021 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".