Assessing Urban Community-Based Palliative Care in Montreal: Identifying Gaps and Opportunities for Quality Improvement
Bibliographic record
Abstract
BackgroundHealth systems increasingly recognize the value of community-based palliative care but there is considerable variability in how such services are delivered. As part of a quality improvement initiative to enhance community-based palliative care, we aimed to characterize publicly accessible services for persons suffering from serious illness in a diverse, large North American city in Canada. We assessed the degree to which structures and processes followed best-practice recommendations of high-quality community-based palliative care.MethodologyWe conducted a cross-sectional survey with healthcare workers to assess structures and processes related to community-based palliative care team composition, care access and provision, care continuity, and care transitions.ResultsCommunity-based palliative care teams in our sample adhered to many best-practice recommendations, such as working in multi-disciplinary teams, providing 24/7 access, and fostering care transitions to and from inpatient palliative care settings. However, access to community-based palliative care was not uniform, and considerable variability existed in prognostic admission criteria. We also identified gaps in psycho-spiritual and personal care support capacity. Specialized, dedicated psycho-spiritual, and personal care support services were missing from more than 75% of community-based palliative care teams.ConclusionsA survey of structures and processes in community-based palliative care teams revealed variability in service organization and care processes. Many services lacked psycho-spiritual and personal care support. Our findings may be representative of similar structural issues elsewhere and suggest the need for broader efforts to understand the system-level factors that shape community-based palliative care service structures and processes.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".