The CaregiverVoice Survey: A Pilot Study Surveying Bereaved Caregivers To Measure the Caregiver and Patient Experience at End of Life
Bibliographic record
Abstract
OBJECTIVE: To integrate patient and caregiver feedback into end-of-life (EOL) care improvement, we tested the feasibility of a standardized, common instrument to measure care experiences across multiple settings in the last three months of life. METHODS: We developed and tested a survey, called the CaregiverVoice survey, which combined two validated questionnaires, the FAMCARE-2 and VOICES-SF. A retrospective, observational design was used to survey bereaved caregivers of decedents who had received homecare services in Ontario, Canada. RESULTS: In total, 330 surveys were completed (overall response rate of 13%, regional rates ranged from 4% to 83%). There was less than 5% missing data. Most patients received care from multiple settings in the last three months of life, including 60% for which a hospital stay was reported. The overall mean of the 19 FAMCARE-2 items was 1.7 (SD 0.7), with 72% of ratings as 1 very satisfied to 2 satisfied. On VOICES-SF items, 6% of respondents rated "all end-of-life services" as fair or poor, 24% as good, and 70% as excellent or outstanding, with variation depending on care site rated. 13% of caregivers reported that pain management was fair or poor in the last week of life. CONCLUSIONS: This pilot study provides preliminary evidence that it is feasible to capture the patient and caregiver experience at EOL using a comprehensive survey, though survey distribution method greatly affected response rates. The majority of responses rated care as excellent or very good, although several specific areas for improvement were identified.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".