Reaching health facilities in situations of emergency: Experiences of pregnant women in Africa’s largest megacity
Bibliographic record
Abstract
Abstract Travel of pregnant women requiring emergency obstetric care (EmOC) to health facilities remains a ‘black box’ of many unknowns to the health system, more so in megacities which are fraught with wide inequalities. This in-depth study on travel of pregnant women in Africa’s largest megacity is based on interviews conducted between September 2019 and January 2020 with 47 women and 11 of their relatives who presented at comprehensive EmOC facilities in situations of emergency, requiring some EmOC services. Despite recognising danger signs, pregnant women are often faced with conundrums on “when”, “where” and “how” to reach EmOC facilities. While the decision-making process is a shared activity amongst all women, the available choice-options vary depending on socio-economic status. Women preferred to travel to facilities deemed to have “nicer” health workers, even if these were farther from home. Reported travel time ranged from 5-240 minutes in daytime and 5-40 minutes at night. Many women reported facing remarkably similar travel experiences, with varied challenges faced in the daytime (traffic congestion) compared to night-time (security concerns and scarcity of public transportation). This was irrespective of their age, socio-economic background, or obstetric history. However, the extent to which this experience impacted on their ability to reach facilities depended on their agency and support systems. Travel experience was better if they had their personal vehicle for travel at night, support of relatives or direct/indirect connections with senior health workers at comprehensive EmOC facilities. Referral barriers between facilities further prolonged delays and increased cost of travel for many women. If the goal to leave no one behind remains a priority, in addition to other health systems strengthening interventions, referral systems need to be improved, advocacy on policies to encourage women to utilise nearby functional facilities when in situations of emergency and private sector partnerships should be explored.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".