Why women die after reaching the hospital: a qualitative critical incident analysis of the ‘third delay’ in postconflict northern Uganda
Bibliographic record
Abstract
OBJECTIVES: To critically explore and describe the pathways that women who require emergency obstetrics and newborn care (EmONC) go through and to understand the delays in accessing EmONC after reaching a health facility in a conflict-affected setting. DESIGN: This was a qualitative study with two units of analysis: (1) critical incident technique (CIT) and (2) key informant interviews with health workers, patients and attendants. SETTING: Thirteen primary healthcare centres, one general private-not-for-profit hospital, one regional referral hospital and one teaching hospital in northern Uganda. PARTICIPANTS: Forty-nine purposively selected health workers, patients and attendants participated in key informant interviews. CIT mapped the pathways for maternal deaths and near-misses selected based on critical case purposive sampling. RESULTS: After reaching the health facility, a pregnant woman goes through a complex pathway that leads to delays in receiving EmONC. Five reasons were identified for these delays: shortage of medicines and supplies, lack of blood and functionality of operating theatres, gaps in staff coverage, gaps in staff skills, and delays in the interfacility referral system. Shortage of medicines and supplies was central in most of the pathways, characterised by three patterns: delay to treat, back-and-forth movements to buy medicines or supplies, and multiple referrals across facilities. Some women also bypassed facilities they deemed to be non-functional. CONCLUSION: Our findings show that the pathway to EmONC is precarious and takes too long even after making early contact with the health facility. Improvement of skills, better management of the meagre human resource and availing essential medical supplies in health facilities may help to reduce the gaps in a facility's emergency readiness and thus improve maternal and neonatal outcomes.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".