Pediatric out-of-hospital deaths following hospital discharge: a mixed-methods study
Bibliographic record
Abstract
BACKGROUND: Out-of-hospital death among children living in resource poor settings occurs frequently. Little is known about the location and circumstances of child death following a hospital discharge. OBJECTIVES: This study aimed to understand the context surrounding out-of-hospital deaths and the barriers to accessing timely care for Ugandan children recently discharged from the hospital. METHODS: This was a mixed-methods sub-study within a larger cohort study of post-discharge mortality conducted in the Southwestern region of Uganda. Children admitted with an infectious illness were eligible for enrollment in the cohort study, and then followed for six months after discharge. Caregivers of children who died outside of the hospital during the six month post-discharge period were eligible to participate in this sub-study. Qualitative interviews and univariate logistic regression were conducted to determine predictors of out-of-hospital deaths. RESULTS: Of 1,242 children discharged, 61 died during the six month post-discharge period, with most (n=40, 66%) dying outside of a hospital. Incremental increases in maternal education were associated with lower odds of out-of-hospital death compared to hospital death (OR: 0.38, 95% CI: 0.19 - 0.81). The qualitative analysis identified health seeking behaviors and common barriers within the post-discharge period which delayed care seeking prior to death. For recently discharged children, caregivers often expressed hesitancy to seek care following a recent episode of hospitalization. CONCLUSION: Mortality following discharge often occurs outside of a hospital context. In addition to resource limitations, the health knowledge and perceptions of caregivers can be influential to timely access to care. Interventions to decrease child mortality must consider barriers to health seeking among children following hospital discharge.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".