Planning for Hospital Discharge for Older Adults in Uganda: A Qualitative Study Among Healthcare Providers Using the COM-B Framework
Bibliographic record
Abstract
Background: Proper discharge planning enhances continuity of patient care, reduces readmissions, and ensures safe and timely transition from health facility to home-based care. The current study aimed at exploring the healthcare providers' perspectives of discharge planning among older adults, with respect to barriers and facilitators within the Ugandan health system. Methods: We conducted a qualitative exploratory study that used one-on-one interviews (Additional file 1) to describe individual perspectives of healthcare providers in their routine clinical care setting. The study included medical doctors (including consultants and physicians), nurses and physiotherapists directly involved in providing care to older adults. We conducted 25 in-depth interviews among healthcare providers for older adults with non-communicable diseases. The audio-recorded interviews were transcribed verbatim. Data were manually organized using a framework matrix guided by the COM-B domains (capability, opportunity and motivation) as the broad themes and sub-themes (physical and psychological capability, social and physical opportunity, reflective and automatic motivation) that influence behavior change (discharge planning). Results: Discharge planning was facilitated by availability of discharge forms, continuous medical education and working experience. The barriers to discharge planning were understaffing, workload/insufficient time, lack of discharge planning guidelines, lack of multidisciplinary approach and congested inpatient wards. Both barriers and facilitators were at various levels of healthcare service delivery such as patient, caregiver, healthcare provider, health facility and policy levels. Conclusion: Barriers to discharge planning spread across all levels of healthcare service delivery, but they can be addressed by enhancing the facilitators. This calls for a multi-level action to ensure adequate and quality patient care during and after hospitalization.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".