Interprofessional Teams Supporting Care Transitions from Hospital to Community: A Scoping Review
Bibliographic record
Abstract
Introduction: Poor outcomes following the transition from hospital back to community living are common, especially for older adults with complex health and social care needs. Some health care systems now have multiple interprofessional teams (in hospital and community) to support care transitions. These teams will need to be well coordinated to improve care transition outcomes. Methods: We conducted a scoping review to identify and map peer-reviewed literature on how interprofessional teams are working together to support older adults transitioning from hospital back to the community. We used the six-stage framework developed by Levac and colleagues (2010). Procedures were guided by the Joanna Briggs Institute scoping review guidelines. Results: Our structured search and screening process resulted in 70 articles, published between 2000 and 2022, from 14 counties. Within these articles, 26 programs were described that used interprofessional teams in both the hospital and community. Discussion: The qualitative articles suggested that effective teamwork is very important for promoting care transition quality, but the quantitative research did not report on team-related outcomes. Quantitative research has described, but not evaluated, strategies for promoting interprofessional collaboration. Conclusion: Future research should focus on evaluating processes used to promote effective interprofessional teamwork in care transition interventions.
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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 | 0.021 | 0.083 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".