Length of stay of hospitalized patients at tertiary psychiatry facilities in Uganda: the role of caregiver’s presence
Bibliographic record
Abstract
Abstract Background Whether the presence of caregivers during the hospital stay of patients with mental illness affects the length of hospital stay (LoS) remains inconclusive. Aims (1) To determine the average LoS and the associated factors, and (2) to determine the role of caregivers’ presences during inpatient stay on LoS. Methods We conducted a cross-sectional study in two hospitals in Uganda; one with caregivers and the other without caregivers between July to November 2020. Mann-Whitney U test was used to compare LoS in the two selected hospitals and linear regression was used to determine factors associated with LoS. Results A total of 222 participants were enrolled, the majority were males (62.4%). Mean age was 36.3 (standard deviation (SD) = 13.1) years. The average LoS was 18.3 (SD = 22.3) days, with patients in a hospital without caregivers having a longer median LoS (i.e., (30 (interquartile range (IQR) = 30) vs. 7 (7) days; χ2 = 68.95, p < 0.001). The factors significantly associated a longer LoS among our study participants included; being admitted in a hospital without caregivers (adjusted coefficient [aCoef]: 14.88, 95% CI 7.98–21.79, p < 0.001), a diagnosis of schizophrenia (aCoef: 10.68, 95 %CI 5.53–15.83, p < 0.001), being separated or divorced (aCoef: 7.68, 95% CI 1.09–14.27, p = 0.023), and increase in money spent during the admission (aCoef: 0.14, 95% CI 0.09–0.18, p < 0.001). Conclusion Patients with mental illness in southwestern Uganda have a short LoS (below 28 days), and the stay was much shorter for patients with fulltime caregivers. We recommend caregivers presence during patient’s hospital stay to reduce the LoS and minimize healthcare expenditure.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".