Risk Management During the Transition From Hospital to Home: A Multiple Case Study Documenting the Experience of Patients Living With a Major Neurocognitive Disorder, Their Caregivers, and Healthcare Professionals
Bibliographic record
Abstract
Understanding the risks in the months following hospital discharge is crucial for healthcare professionals to ensure the need for assistance is met. However, this may be challenging in the case of patients living with a major neurocognitive disorder (PLMNCD). Thus, it is important to incorporate patients' and caregivers' experiences of the transition from hospital to home in the risk assessment. This multiple case study comprised 7 PLMNCD, their caregivers, and occupational therapists. Fifty-four interviews, conducted just before, as well as 3 weeks and 3 to 6 months after hospital discharge, were qualitatively analyzed. Results revealed that risk management during the hospital-to-home transition is a dynamic process aimed at establishing a satisfactory routine while avoiding adverse events. This risk management process, which identifies challenges over time and between stakeholders, involves (a) determining the seriousness and acceptability of risks, (b) reflecting on ways to manage risks, and (c) taking steps to manage risks. This knowledge will help to provide more appropriate care and services that strike a balance between safety and autonomy.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".