Expanding our understanding of factors impacting delayed hospital discharge: Insights from patients, caregivers, providers and organizational leaders in Ontario, Canada
Bibliographic record
Abstract
INTRODUCTION: The purpose of this paper was to understand the nature of delayed hospital discharge through the lens of a policy framework (ideas, institutions and interests; 3-I framework). MATERIALS AND METHODS: One-to-one in-depth interviews were conducted with 57 participants, including 18 patients, 18 caregivers, 11 providers and 10 organizational leaders across two hospital networks in urban and rural regions of Ontario, Canada. RESULTS: Delayed discharge was a product of spill-over effects (due to rules and eligibility in other health sectors) and variable implementation of policies and guidelines (institutions); competing priorities and tensions among patients, caregivers, providers and organizational leaders (interests); as well as a number of perceived root causes including patient complexity, caregiver burnout, lack of system infrastructure, and an imbalance of system and personal responsibility to support aging adults (ideas). CONCLUSIONS: The 3-I framework allowed us to examine the contributing factors to delayed discharge in a comprehensive way. Based on our findings we suggest that cross-sectoral collaboration and strengthening of relationships among stakeholders is required to address this complex policy problem.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".