The development of specialized palliative care in the community: A qualitative study of the evolution of 15 teams
Bibliographic record
Abstract
BACKGROUND: Interprofessional specialized palliative care teams at home improve patient outcomes, reduce healthcare costs, and support many patients to die at home. However, practical details about how to develop home-based teams in different regions and health systems are scarce. AIM: To examine how a variety of home-based specialized palliative care teams created and grew their team over time and to identify critical steps in their evolution. DESIGN: A qualitative study was designed based on a grounded theory approach, using semi-structured interviews and other documentation. SETTING/PARTICIPANTS: In all, 15 specialized palliative care teams from Ontario, Canada, representing rural and urban areas. Data were collected from core members of the teams, including nurses, physicians, personal support workers, spiritual counselors, and administrators. RESULTS: In all, 122 individuals where interviewed, ranging from 4 to 10 per team. The analysis revealed four stages in team evolution: Inception, Start-up (n = 4 teams), Growth (n = 5), and Mature (n = 6). In the Inception stage, a champion provider was required to leverage existing resources to form the team. Start-up teams were testing and adjusting care processes to solidify their presence in the community. Growth teams had core expertise, relationships with fellow providers, and 24/7 support. Mature teams were fully integrated in the community, but still engaged in continuous quality improvement. CONCLUSION: Understanding the developmental stages of teams can help to inform the progress of other community-based teams. Appropriate outcome measures at each stage are also critical for team motivation and steady progress.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".