Predicting maternal healthcare seeking behaviour in Afghanistan: exploring sociodemographic factors and women’s knowledge of severity of illness
Bibliographic record
Abstract
BACKGROUND: Little is known whether women's knowledge of perceived severity of illness and sociodemographic characteristics of women influence healthcare seeking behavior for maternal health services in Afghanistan. The aim of this study was to address this knowledge gap. METHODS: Data were used from the Afghanistan Health Survey 2018. Women's knowledge in terms of danger signs or symptoms during pregnancy was assessed. The signs or symptoms were bleeding, swelling of the body, headache, fever, or any other danger sign or symptom (e.g., high blood pressure). A categorical variable of knowledge score was created. The outcome variables were defined as ≥ 4 ANC vs. 0-3 ANC; ≥ 4 PNC vs. 0-3 PNC visits; institutional vs. non-institutional deliveries. A multivariable generalized linear model (GLM) was used. RESULTS: Data were used from 9,190 ever-married women, aged 13-49 years, who gave birth in the past two years. It was found that 56%, 22% and 2% of women sought healthcare for institutional delivery, ≥ 4 ANC, ≥ 4 PNC visits, respectively, and that women's knowledge is a strong predictor of healthcare seeking [odds ratio (OR)1.77(1.54-2.05), 2.28(1.99-2.61), and 2.78 (2.34-3.32) on knowledge of 1, 2, and 3-5 signs or symptoms, respectively, in women with ≥ 4 ANC visits when compared with women who knew none of the signs or symptoms. In women with ≥ 4 PNC visits, it was 1.80(1.12-2.90), 2.22(1.42-3.48), and 3.33(2.00-5.54), respectively. In women with institutional deliveries, it was 1.49(1.32-1.68), 2.02(1.78-2.28), and 2.34(1.95-2.79), respectively. Other strong predictors were women's education level, multiparity, residential areas (urban vs. rural), socioeconomic status, access to mass media (radio, TV, the internet), access of women to health workers for birth, and decision-making for women where to deliver. However, age of women was not a strong predictor. CONCLUSION: Our findings suggest that pregnant women's healthcare seeking behaviour is influenced by women's knowledge of danger signs and symptoms during pregnancy, women's education, socioeconomic status, access to media, husband's, in-laws' and relatives' decisions, residential area, multiparity, and access to health workers. The findings have implications for promoting safe motherhood and childbirth practices through improving women's knowledge, education, and social status.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".