“There’s No Room for Silos.” Interprofessional Education in Hospital to Home Integrated Care Programs
Bibliographic record
Abstract
Introduction: Interprofessional education (IPE) is critical for training health and social care providers and building workforce capacity for integrated care. This paper reports key informants’ descriptions of IPE in training existing health care professionals to work in hospital to home integrated care programs in Ontario Canada. Method: Utilizing a qualitative descriptive approach, 13 interviews were conducted with leaders of integrated care programs across the province. Data analysis employed a thematic analysis approach. Findings were interpreted through the lens of an interprofessional learning continuum model and competencies for integrated care. Results: Formal and informal IPE within the integrated care programs can support competency development (e.g., role clarity, communication, and teamwork) for interprofessional practice within hospital to home integrated care programs. Key informants acknowledged the importance of cross sector IPE to understand patient care trajectories and provider roles more fully. Discussion: The findings can inform future IPE programs and initiatives to enhance workforce capacity for integrated care. Implications for Education and Practice To prepare future health care providers (HCPs) to work in integrated care, it is important to include IPE and integrated care concepts/principles in formal academic training and offer student placements within established integrated care programs to facilitate learning and competencies early in their career. There’s no room for siloed approaches. IPE within and across health sectors can help health care providers understand the focus of the integrated care program (e.g., patient pathways, referrals) and the roles and responsibilities of various team members. IPE in academic and practice settings should include content related to teamwork competencies, principles for collaborative practice, and foundations of integrated care.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".