A qualitative study exploring hospital-based team dynamics in discharge planning for patients experiencing delayed care transitions in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: In attempt to improve continuity of patient care and reduce length of stay, hospitals have placed an increased focus on reducing delayed discharges through discharge planning. Several benefits and challenges to team-based approaches for discharge planning have been identified. Despite this, professional hierarchies and power dynamics are common challenges experienced by healthcare providers who are trying to work as a team when dealing with delayed discharges. The objective of this study was to explore what was working well with formal care team-based discharge processes, as well as challenges experienced, in order to outline how teams can function to better support transitions for patients experiencing a delayed discharge. METHODS: We conducted a descriptive qualitative study with hospital-based healthcare providers, managers and organizational leaders who had experience with delayed discharges. Participants were recruited from two diverse health regions in Ontario, Canada. In-depth, semi-structured interviews were conducted in-person, by telephone or teleconference between December 2019 and October 2020. All interviews were recorded and transcribed. A codebook was developed by the research team and applied to all transcripts. Data were analyzed inductively, as well as deductively through directed content analysis. RESULTS: We organized our findings into three main categories - (1) collaboration with physicians makes a difference; (2) leadership should meaningfully engage with frontline providers and (3) partnerships across sectors are critical. Regular physician engagement, as equal members of the team, was recommended to improve consistent communication, relationship building between providers, accessibility, and in-person communication. Participants highlighted the need for a dedicated senior leader who ensured members of the team were treated as equals and advocated for the team. Improved partnerships across sectors included the enhanced integration of community-based providers into discharge planning by placing more focus on collaborative practice, combined discharge planning meetings, and having embedded and physically accessible care coordinators in the hospital. CONCLUSIONS: Team-based approaches for delayed discharge can offer benefits. However, to optimize how teams function in supporting these processes, it is important to consistently collaborate with physicians, ensure senior leadership engage with and seek feedback from frontline providers through co-design, and actively integrate the community sector in discharge planning.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.020 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".