Validation of a modified VOICES survey to measure end-of-life care quality: the CaregiverVoice survey
Bibliographic record
Abstract
BACKGROUND: Measuring the care experience at end-of-life (EOL) to inform quality improvement is a priority in many countries. We validated the CaregiverVoice survey, a modified version of the VOICES questionnaire, completed by bereaved caregivers to capture perceptions of care received in the last three months of a patient's life. METHODS: We conducted a retrospective survey of bereaved caregivers representing palliative care patients who died in a residential hospice and/or received palliative homecare in Ontario, Canada. Statistical analyses were completed to establish construct and concurrent validity, as well as reliability of the survey. RESULTS: Responses were obtained from 906 caregivers: 330 surveyed from homecare agencies and 576 from hospices. The CaregiverVoice survey demonstrated concurrent validity in scores correlating to FAMCARE2 items, and construct validity in performing according to expected patterns, e.g., correlation of scores to qualitative perceptions and significant variability based on care contexts such as place of death and setting of care (p < 0.01). Reliability was exhibited in good inter-item correlation of ratings for specific care settings and no significant differences in ratings regardless of whether up to a year had passed since death of patient. CONCLUSIONS: The CaregiverVoice survey demonstrated validity and reliability in the populations assessed. This survey represents one common measure that can be standardized across multiple care settings and is useful for assessing the care experience that can help inform local and national quality improvement activities.
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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 | 0.016 | 0.023 |
| 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 | 0.001 |
| 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".