Facilitators and Barriers to Implementing Transitional Care Managers Within a Public Health Care System
Bibliographic record
Abstract
Transitional care is crucial to ensure quality of care and safety for elderly patients. In the context of health care reforms promoting a shift from a hospital-centered approach to a home care approach, transitional care becomes a vital component and social workers can play an important role in easing transitions. Most recent studies have focused on the development or improvement of transitional care intervention models or tools, but few have addressed implementation issues. In this study, the implementation process of an innovative intervention aiming to integrate transitional care managers (TCMs) from Health and Social Services Centres (HSSC) within two Canadian hospitals was evaluated. Data collection comprised focus groups (n = 8), direct observations, meeting minutes, activity grids and logbooks. To facilitate the implementation of TCMs, decisions were made to clearly indicate their involvement in patients' files and concentrated their efforts on a restricted number of units. Barriers included confusion about target clientele, inequitable information exchange between partners, limited powers regarding coordination of care, and organizational constraints limiting additional measures to improve transitional care. Evaluating implementation processes is crucial to efficiently identify obstacles and apply additional implementation strategies to promote the integration of new practices within the health care system.
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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 | 0.035 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".