The family medicine based virtual ward: Qualitative description of the implementation process
Bibliographic record
Abstract
Purpose: Chronically ill older patients transitioning from hospital to home are at increased risk of readmission and complications. Numerous transitional care interventions have been proposed to improve communication and continuity of care throughout the transition. Evidence suggests that the risk of readmission and complications is reduced when interventions provide closer follow-up and multidisciplinary care. Informed by this evidence, the family medicine based virtual ward was developed by a Montreal family medicine group (FMG) to provide home-based care for patients with an elevated risk of emergency department (ED) visit or hospital readmission. The virtual ward team provides comprehensive, multidisciplinary post-discharge care at patients’ homes, combining the systems and staffing of the FMG’s home care program with a nurse case manager. Research was conducted to inform the implementation of similar transitional care programs in other Quebec health care settings. Methods: This research consists of a retrospective qualitative descriptive study of the implementation of the family medicine based virtual ward. Data was obtained from a semi-structured group interview with the team and informal interviews with individual members. Inductive thematic content analysis was used. Results: The following were identified as conditions for its successful implementation: 1) funding, 2) home care, 3) communication, 4) protocol standardization, and 5) continuous quality improvement. Conclusions: This intervention addresses the care of frequent health system users and compensates for gaps in communication and coordination. It was well-received by patients, healthcare providers and health system administrators and has the potential to reduce readmissions and reduce health system costs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".