Perceptions of Facilitators and Barriers to Measuring and Improving Quality in Palliative Care Programs
Bibliographic record
Abstract
OBJECTIVE: To examine perceptions of facilitators and barriers to quality measurement and improvement in palliative care programs and differences by professional and leadership roles. METHODS: We surveyed team members in diverse US and Canadian palliative care programs using a validated survey addressing teamwork and communication and constructs for educational support and training, leadership, infrastructure, and prioritization for quality measurement and improvement. We defined key facilitators as constructs rated ≥4 (agree) and key barriers as those ≤3 (disagree) on 1 to 5 scales. We conducted multivariable linear regressions for associations between key facilitators and barriers and (1) professional and (2) leadership roles, controlling for key program and respondent factors and clustering by program. RESULTS: We surveyed 103 respondents in 11 programs; 45.6% were physicians and 50% had leadership roles. Key facilitators across sites included teamwork, communication, the implementation climate (or environment), and program focus on quality improvement. Key barriers included educational support and incentives, particularly for quality measurement, and quality improvement infrastructure such as strategies, systems, and skilled staff. In multivariable analyses, perceptions did not differ by leadership role, but physicians and nurse practitioners/nurses/physician assistants rated most constructs statistically significantly more negatively than other team members, especially for quality improvement (6 of the 7 key constructs). CONCLUSIONS: Although participants rated quality improvement focus and environment highly, key barriers included lack of infrastructure, especially for quality measurement. Building on these facilitators and measuring and addressing these barriers might help programs enhance palliative care quality initiatives' acceptability, particularly for physicians and nurses.
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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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".