Reflecting on Palliative Care Integration in Canada: A Qualitative Report
Bibliographic record
Abstract
Studies have identified integrated interdisciplinary care as a hallmark of effective palliative care. Although models attempt to show how integration may function, there is little literature available that practically explores how integration is fostered and maintained. In this study we asked palliative care clinicians across Canada to comment on how services are integrated across the healthcare system. This is an analysis of qualitative data from a larger study, wherein clinicians provided written responses regarding their experiences. Content analysis was used to identify response categories. Clinicians (n = 14) included physicians, a nurse and a social worker from six provinces. They identified the benefits of formalized relationships and collaboration pathways with other services to streamline referral and consultation. Clinicians perceived a need for better training of residents and primary care physicians in the community and more acceptance, shared understanding, and referrals. Clinicians also described integrating well with oncology departments. Lastly, clinicians considered integration a complex process with departmental, provincial, and national involvement. The needs and strengths identified by the clinicians mirror the qualities of successfully integrated palliative care programs globally and highlight specific areas in policy, education, practice, and research that could benefit those in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.032 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".