CARE TRANSITIONS FROM HOSPITAL TO HOME: PERSPECTIVES FROM OLDER ADULTS, CAREGIVERS, AND HEALTHCARE PROFESSIONALS
Bibliographic record
Abstract
Abstract Background Saudi Arabia switched from hospital-focused to home-based care for older persons with long-term care needs due to a 25% increase in acute hospital bed occupancy for non-acute older adults. Healthcare professionals, older adults, and families are affected by the change in the care transition. Improvement in health outcomes depends on understanding all three groups. Aim This study aims to investigate the hospital-to-home transition of older adults with chronic conditions. The objective is to identify factors that facilitate or hinder the transition, with the aim of improving care during this critical phase. Methods The research employed a descriptive case study analysed with thematic analysis and an institutional ethnography. Pre- and post-discharge data were obtained from older adults, carers, and healthcare workers at King Saud Medical City using semi-structured interviews, focus groups, participatory observation, and documentary analysis. Findings The findings indicate that culture, family, and national healthcare policies play a crucial role. Older adults and their caregivers encounter obstacles during the transition process, including gender segregation causing difficulties with care coordination. The lack of standardised post-discharge follow-up and fragmented care provision further contribute to these hurdles. Conclusion The research recommends creating a unified administration system as a necessary foundation for solving the largest problems in the transition. This may help Saudi Arabia’s care policymakers and planners create a family-centred healthcare system. Future research should focus on targeted strategies to improve the care transition experiences of older adults and their caregivers.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".