Measuring Family Members’ Satisfaction with End-of-Life Care in Long-Term Care: Adaptation of the CANHELP Lite Questionnaire
Bibliographic record
Abstract
RATIONALE: Improving end-of-life care (EOLC) in long-term care (LTC) homes requires quality measurement tools that assess families' satisfaction with care. This research adapted and pilot-tested an EOLC satisfaction measure (Canadian Health Care Evaluation Project (CANHELP) Lite Questionnaire) for use in LTC to measure families' perceptions of the EOLC experience and to be self-administered. METHODS AND RESULTS: . A literature review identified key domains of satisfaction with EOLC in LTC, and original survey items were assessed for inclusiveness and relevance. Items were modified, and one item was added. PHASE 2: The revised questionnaire was administered to 118 LTC family members and cognitive interviews were conducted. Further modifications were made including reformatting to be self-administered. PHASE 3: The new instrument was pilot-tested with 134 family members. Importance ratings indicated good content and face validity. Cronbach's alpha coefficients (range: .88-.94) indicated internal consistency. CONCLUSION: This research adapted and pilot-tested the CANHELP for use in LTC. This paper introduces the new, valid, internally consistent, self-administered tool (CANHELP Lite Family Caregiver LTC) that can be used to measure families' perceptions of and satisfaction with EOLC. Future research should further validate the instrument and test its usefulness for quality improvement and care planning.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".