Understanding patients’ decision to leave hospital care in Ghana: clinical cases and underlying determinants
Bibliographic record
Abstract
BACKGROUND: The quality of patient discharge teaching and information influences most patients' readiness for discharge and perceptions of care. Planned patient discharge positively impacts patient health outcomes and post-discharge care management. However, some patients withdraw from care before being formally discharged, often termed discharge against medical advice (DAMA), among other labels. Patient withdrawal from care occurs in some Ghanaian hospitals, yet this phenomenon is understudied. We present clinical cases of this phenomenon in a Ghanaian hospital to understand why patients and their families leave hospital care before formal discharge. METHODS: Data was obtained through interviews, a focus group, and participant observations from nurses, patients, and caregivers. Thematic analysis and ethnographic case mapping helped us to identify patient discharge types and five DAMA cases. RESULTS: The underlying factors for discharge in these cases were identified and interpreted. These included health beliefs and cultural norms, costs of care, low health literacy, length of hospital stay and recovery outcomes. Others were social responsibility demands and lack of medical specialists and equipment. A detailed interrogation of the clinical cases and underlying factors revealed the need to reconceptualize discharge against medical advice. CONCLUSION: We recommend that providers embrace dialogue, cultural competency, and person-centered care and communication in managing patients' decisions respecting discharge. We reason that discharge against medical advice is a quality gap requiring both patient rights and ethical lense to address.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".