Risks Perceived by Frail Male Patients, Family Caregivers and Clinicians in Hospital: Do they Change after Discharge? A Multiple Case Study
Bibliographic record
Abstract
INTRODUCTION: Up to 40% of hospitalised seniors are frail and most want to return home after discharge. Inaccurate estimation of risks in the hospital may lead to inadequate support at home. This study aimed to document convergences and divergences between risks and support needs identified before hospital discharge and perceived at home post-discharge. METHODS: This research used a multiple case study design. Three cases were recruited, each involving a hospitalised frail patient aged 70+, the main family caregiver and most of the clinicians who assessed the patient before and after hospital discharge. Thirty-two semi-structured interviews were conducted and their transcripts analysed using a qualitative thematic analysis approach. RESULTS: Among risks raised by participants, falls were the only one with total inter-participant/inter-time/inter-case convergence. In all cases, all participants mentioned, before and after discharge, home adaptations and use of technical aids to mitigate this risk. However, clinicians recommended professional services while patients and family caregivers preferred to rely on family members and their own coping strategies. CONCLUSION: The divergences identified for most risks and support needs between users and clinicians, before and after discharge, provide new insights into a comprehensive and patient-centred risk assessment process to plan hospital discharge for frail elderly.
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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 | 0.001 |
| 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".