Women’s knowledge of perceived severity of illness predicting healthcare seeking behaviour for antenatal, postnatal care, and institutional delivery services. Findings from a National Survey in Afghanistan
Bibliographic record
Abstract
Abstract Background The importance of healthcare seeking for women’s health is well documented. However, less is known how women’s knowledge of perceived severity of illness affects healthcare seeking behaviour. This study examined the associations of women’s knowledge of perceived severity of illness with healthcare seeking behaviour for maternal health services. Methods Data were used from the Afghanistan Health Survey 2018. Women’s knowledge in terms of knowing danger signs or symptoms related to maternal health was assessed. The signs or symptoms a woman was expected to name were bleeding, swelling of the body, headache, fever, or any other danger sign or symptom (e.g., high blood pressure). A categorical variable on knowledge 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 multivariate regression model was applied. 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 only 22% and 2% of women sought healthcare for ≥ 4 ANC, ≥ 4 PNC visits, respectively. Fifty six percent of women had institutional deliveries. Multivariate analysis showed that the odds ratios (ORs) for ANC visits were 1.76(95%CI;1.53–2.04), 2.25(95%CI;1.97–2.58), and 2.81 (95%CI:2.35–3.35) in women who knew 1, 2, and 3–5 signs or symptoms, respectively, compared to women who knew none. The ORs for PNC visits were 1.81(95%CI:1.12–2.93), 2.22(95%CI:1.42–3.48), and 3.37(95%CI:2.02–5.62) in women who knew 1, 2, and 3–5 signs or symptoms, respectively, compared to women who knew none. The ORs for institutional deliveries were 1.38(95%CI:1.22–1.56), 1.80(95%CI:1.59–2.04), and 1.97(95%CI:1.64–2.37) in women who knew 1, 2, and 3–5 signs or symptoms, respectively, compared to women who knew none. It was found that in women who did not use at least 4 ANC, 4 PNC visits, or institutional deliveries, 27%, 33%, and 23% of them, respectively, said that it was unnecessary to seek healthcare. Main perceived barriers mentioned, were distance to clinics, financial constraints, and lack of female staff. Conclusion Health interventions are needed to promote women’s knowledge of perceived severity of illness, and to address perceived barriers in accessing maternal health services.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".