Evaluation of the Measuring and Improving Quality in Palliative Care Survey
Bibliographic record
Abstract
PURPOSE: To evaluate the reliability, content validity, and variation among sites of a survey to assess facilitators and barriers to quality measurement and improvement in palliative care programs. METHODS: We surveyed a sample of diverse US and Canadian palliative care programs and conducted postcompletion discussion groups. The survey included constructs addressing educational support and training, communication, teamwork, leadership, and prioritization for quality measurement and improvement. We tested internal consistency reliability, described variation among sites, and reported descriptive feedback on content validity. RESULTS: Of 103 respondents in 11 sites, the most common roles were attending physician (38.9%) and nurse practitioner, clinical nurse specialist, or physician assistant (16.5%). Internal consistency reliability was acceptable (Cronbach's α = .70 to .99) for all but one construct. Results varied across sites by more than 1 point on the 1 to 5 scales between the 10th and 90th percentiles of sites for two constructs in recognition and focus on quality measurement (score range by site, 1.7 to 4.8), one construct in teamwork (score range, 3.1 to 4.6), and five constructs in quality improvement (score range, 1.8 to 4.6). In descriptive content validity evaluation, respondents described the survey as an opportunity for assessing quality initiatives and discussing potential improvements, particularly improvements in communication, training, and engagement of team members regarding program quality efforts. CONCLUSION: This survey to assess palliative care team perspectives on barriers and facilitators for quality measurement and improvement demonstrated reliability, content validity, and initial evidence of variation among sites. Our findings highlight how palliative care team members' perspectives may be valuable to plan, evaluate, and monitor quality-of-care initiatives.
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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 | 0.040 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".