Supporting Transitions from Hospital to Home by Engaging Volunteers of Third Sector Organizations: A Scoping Review
Bibliographic record
Abstract
Introduction: The transition from hospital to home is a critical clinical juncture marked by significant risks. Third Sector Organizations (TSOs) are well-positioned to support these transitions through volunteer-based programs. Given the increasing complexity of patient needs and the push for reduced hospital lengths of stay, the integration of community resources into transitional care becomes vital. Objective: Study objectives were i) to identify where TSOs are engaged in supporting post-hospital transitions, ii) to document the characteristics of transitional care models delivered by TSOs, and iii) to characterize the clients participating in these volunteer-supported programs. Methods and Results: Forty-eight articles that reported on a community-based program delivered by a third-sector organization supporting adults transitioning from hospital to home were included. Study results suggest that TSOs can fill critical gaps in transitional care by leveraging local knowledge and providing personalized, practical, and psychosocial support. TSOs leveraged volunteers to offer personalized, community-based support that addressed both practical and psychosocial needs during care transitions; however, significant variability in program structure and limited evaluation data hindered the assessment of effectiveness and transferability. All programs were time-limited, engaged volunteers in service delivery, and provided home-based and community-based services. Conclusions: This review highlights the importance of integrating volunteers and TSOs into health systems to develop a more comprehensive approach to transitional care. However, the scalability of volunteer and third-sector-facilitated programs may be challenged by a lack of consistency in programs and reporting, which can undermine transferability and evidence-based practice.
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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 | 0.013 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".