Discharge policies and care practices for children with suspected sepsis: A health facility scan at a nationally representative sample of hospitals and health centres in Uganda
Bibliographic record
Abstract
Under-five children in low- and middle-income countries remain at high risk of death after hospital discharge. However, few studies have systematically assessed discharge processes or facility readiness to support safe transitions of care. This study aimed to assess health facility readiness to provide pediatric discharge care for children under five years of age and current discharge practices in a nationally representative sample of health facilities in Uganda. A cross-sectional health facility scan was conducted between October 2020 and May 2021 at 36 facilities providing inpatient pediatric care in Uganda. Primary outcomes included: (1) facility readiness for pediatric discharge, defined as availability of infrastructure, technology, forms/job aids, and equipment; and (2) observed discharge care practices, including caregiver counselling, provision of take-home materials, post-discharge risk assessment, referrals, and clinical assessments on the day of discharge. Secondary outcomes included discrepancies between reported versus observed discharge care practices, discharge relevant admission practices, as well as caregiver and health worker satisfaction. Thiry-six health facilities were enrolled and 180 pediatric discharge observations, 180 caregivers, and 180 health workers were assessed. Hospitals had higher readiness scores in infrastructure (p < 0.001), technology (p = 0.006), and equipment (p = 0.021) than health centres. Hospitals also performed better in the provision of discharge risk assessment, clinical assessment on the day of discharge, follow-up, and provision of take-home materials. In contrast, health centres more consistently provided discharge counselling (p = 0.021) and had higher counselling topic scores (p < 0.001). Overall, 82.8% of discharges included a clinical assessment, 31.1% included a follow-up referral, and 26.1% included a risk assessment. Observed practices often diverged from reported procedures. These findings identified several priority areas for quality improvement in both resource availability and discharge care delivery in all settings. Standardizing discharge policies and tools may strengthen discharge care and may be used as a guide to inform national-level pediatric discharge policies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".