STAFF PERSPECTIVE ON VIRTUAL TEAM-BASED CARE IN THE PROCESS OF TRANSITION TO LONG-TERM CARE
Bibliographic record
Abstract
Abstract Transitioning from hospital to long-term care (LTC) is a vulnerable time for older patients and families. Successful care transitions require interdisciplinary team and cross-sectoral coordination, as well as engagement of patients and families in care planning. This study aimed to identify enablers and barriers to delivering virtual team-based care to support older adult care transitions from hospital to LTC. Patton’s utilization-focused approach informed the study design. Our research methods included semi-structured interviews, focus groups, and organizational policy reviews. The study involved 52 multidisciplinary team members, including nurses, physicians, rehabilitation practitioners, healthcare leaders, and older patients and family members. Thematic analysis identified key enablers (team engagement, training and support, and access to digital equipment) and barriers (technology infrastructure, resources, privacy and security). Our findings suggest that virtual team-based care is perceived by healthcare staff as acceptable, efficient, and cost-saving. Older persons and their families have complex care needs, and prioritizing their involvement in virtual team-based care planning is key to addressing barriers in the process of care transition success. Clear policies and guidelines are needed to support the integration of virtual team-based care into practice to optimize support to families and patient health outcomes. Future research should investigate effective strategies to include older persons and families through virtual team-based care planning technology.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".