Removing the Silos: Interprofessional Education to Support Hospital-to-Home Integrated Care
Bibliographic record
Abstract
Backgrround: Interprofessional education (IPE) and collaborative practice competencies are foundational for students and professionals working in integrated care teams. Key informants descriptions of IPE training in Ontario hospital-to-home integrated care programs are presented. Study findings offer implications for health professions education in academic programs and continuing professional development in practice settings. Approach: A qualitative descriptive approach was used to interview 3 leaders in integrated care programs across Ontario. Participants were asked about how and what forms of interprofessional education are currently used to train health professionals to work within the current hospital-to-home integrated care programs. Participants described facilitators and barriers to implementing IPE within their programs and offered recommendations to support such initiatives in current practice environments. Thematic analysis was used to elucidate key findings which were interpreted through the lens of an interprofessional learning continuum model and competencies for integrated care. Results: Both formal and informal interprofessional education (IPE) within hospital-to-home integrated care programs can support competency development (e.g. role clarity, communication, teamwork) for interprofessional practice across health sectors. Key Informants acknowledged the importance of cross sector IPE to understand patient care trajectories, and healthcare provider roles more fully. IPE in academic and practice settings should include content about fundamentals of integrated care, teamwork competencies, and principles of collaborative practice. Implications: It cannot be assumed that all health and social care professionals understand the fundamentals of integrated care nor how to work effectively in teams. Developing these competencies should begin in formal academic programs and must continue to the practice setting where teams can continue to develop and grow together with the populations to whom they provide care and service. Next StepsFindings from the present study have informed new undergraduate and graduate curricula - more indepth content about integrated care as been embedded within two professional practice courses. The findings have also informed a new project. We are currently embarking on the development of a co-designed teaching learning initiative to inform an experiential teaching and learning curriculum for community-based integrated stroke care. We are engaging stroke community members including stroke patients, family caregivers, undergraduate and graduate students in co-developing both the content, pedagogical and evaluation approaches.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".