A “Simple” Evidence-Based Intervention to Improve Care Transitions for Frail Patients with Complex Health Conditions: Why Didn’t It Work as Expected?
Bibliographic record
Abstract
The transition from hospital to home is a vulnerable period for patients with complex conditions, who are often frail, at risk for adverse events and unable to navigate a system of poorly coordinated care in the post-discharge period.Care transition interventions are seen as effective care coordinating mechanisms for reducing avoidable adverse events associated with the transition of the patient from the hospital to the home.A study was undertaken to evaluate the effectiveness of a care transition intervention involving a hand-off between a hospital-based care transitions nurse and a communitybased rapid response nurse.Two focus groups were held, one involving rapid response nurses and the other involving care transition nurses.Individual interviews were conducted with the managers (n = 2) and executives (n = 2) to identify the factors that facilitated or were barriers to its implementation.Using thematic content analysis, it was found that the effectiveness of transitional coordination efforts was thwarted by ineffective communication, which affected the quality of the underlying relationships between the two teams.Other barriers to achieving the desired outcomes included the following: issues of role clarity, role awareness and acceptance, the adequacy and reinforcement of coordinating mechanisms, the effectiveness of the information exchange protocols and the absence of shared measures of accountability. Clinical integration initiatives have fewer human resource and financial implementation barriers compared with organizational integration efforts but are complex undertakings requiring clear alignment between organizations, shared accountability measures, effective communication processes and relationships of trust and respect between interprofessional teams.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".