Healthcare professionals’ perceptions of the implementation of the transitional discharge model for community integration of psychiatric clients
Bibliographic record
Abstract
Research has demonstrated the benefits of mental healthcare interventions that ensure a safe transition of clients throughout the discharge and community integration process. This paper reports on qualitative data from focus groups with health professionals collected as part of a larger a mixed method study designed to examine the effectiveness and sustainability of implementing the transitional discharge model. Data collection involved two sets of focus groups, which were held at six months and one-year post-implementation. There were 216 health professional participants from nine (9) hospitals across the Province of Ontario, Canada. Data analysis used a four-step ethnographic framework by Leininger (1985) to identify descriptors and recurrent and major themes. The study identified four major themes, including healthcare professionals' roles and positive experiences in implementing the transitional discharge model; perceived benefits of the model; challenges to implementing the model; and suggestions for sustaining the model's implementation. Healthcare professionals felt that the implementation of the transitional discharge model has the potential for increasing their awareness of the process of clients' integration, serving as a framework for discharge planning, and reducing hospital readmissions. The study findings may provide healthcare providers with information on pragmatic ways to plan clients' discharge, to bridge the gap between hospital and community care, and to positively impact client health outcomes.
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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".