Predictors of prolonged hospitalization after vaginal birth in Ghana: A comparative study
Bibliographic record
Abstract
Early discharge after child delivery although indispensable, but maybe precluded by several factors. The effect of these factors on prolonged length of stay (LOS) after vaginal delivery has been sparsely investigated in Ghana. This limits understanding of potential leading indicators to inform intervention efforts and optimize health care delivery. This study examined factors associated with prolonged LOS after vaginal birth in two time-separated cohorts in Ghana. We analyzed data from Ghana's demographic and health surveys in 2007 and 2017. Our comparative analysis is based on subsamples in 2007 cohort (n = 2,486) and 2017 cohort (n = 8,065). A generalized estimating equation (GEE) with logistic regression was used to examine predictors of prolonged LOS after vaginal delivery. The cluster effect was accounted for using the exchangeable working correlation. The odds ratios (OR) and 95% confidence interval were reported. We found that 62.4% (1551) of the participants in 2007 had prolonged LOS after vaginal delivery, whereas the prevalence of LOS in the 2017 cohorts was 44.9% (3617). This constitutes a 17.5% decrease over the past decade investigated. Advanced maternal age (AOR = 1.24, 95% Cl 1.01-1.54), place of delivery (AOR = 1.18, 95% Cl 1.02-1.37), child's size below average (AOR = 1.14; 95% Cl 1.03-1.25), and problems suffered during/after delivery (AOR = 1.60; 95% Cl 1.43-1.80) were significantly associated with prolonged (≥ 24 hours) length of hospitalization after vaginal delivery in 2017. However, among variables that were available in 2007, only those who sought delivery assistance from non-health professionals (AOR = 1.89, 95% CI: 1.00-3.61) were significantly associated with prolonged LOS in the 2007 cohort. Our study provides suggestive evidence of a reduction in prolonged LOS between the two-time points. Despite the reduction observed, more intervention targeting the identified predictors of LOS is urgently needed to further reduce post-vaginal delivery hospital stay. Also, given that LOS is an important indicator of medical services use, an accurate understanding of its prevalence and associated predictors are useful in assessing the efficiency of hospital management practices and the quality of care of patients in Ghana.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".