Interprofessional team building in the palliative home care setting: Use of a conceptual framework to inform a pilot evaluation
Bibliographic record
Abstract
Home-based palliative care is increasingly dependent on interprofessional teams to deliver collaborative care that more adequately meets the needs of clients and families. The purpose of this pilot evaluation was to qualitatively explore the views of an interprofessional group of home care providers (occupational therapists, nurses, personal support work supervisors, community care coordinators, and a team coordinator) regarding a pilot project encouraging teamwork in interprofessional palliative home care services. We used qualitative methods, informed by an interprofessional conceptual framework, to analyse participants' accounts and provide recommendations regarding strategies for interprofessional team building in palliative home health care. Findings suggest that encouraging practitioners to share past experiences and foster common goals for palliative care are important elements of team building in interprofessional palliative care. Also, establishing a team leader who emphasises sharing power among team members and addressing the need for mutual emotional support may help to maximise interprofessional teamwork in palliative home care. These findings may be used to develop and test more comprehensive efforts to promote stronger interprofessional teamwork in palliative home health care delivery.
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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 | 0.083 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".