Health insurance coverage and maternal healthcare services utilization in Jordan: evidence from the 2017–18 Jordan demographic and health survey
Bibliographic record
Abstract
BACKGROUND: Despite the relationship between health insurance coverage and maternal healthcare services utilization, previous studies in Jordan on the use of maternal healthcare services have mainly focused on patterns and determinants of maternal healthcare services utilization in Jordan. Therefore, this study investigated the relationship between health insurance coverage and maternal healthcare services utilization in Jordan. METHODS: This study used secondary data published in 2017-18 Jordan Demographic and Health Survey on 4656 women of reproductive age (15-49 years). The independent variable was health insurance coverage and the outcome variable was maternal healthcare services utilization, measured through timing of first antenatal visit, four or more antenatal care visits, and skilled birth attendance. The data were analyzed using descriptive statistics and binary logistic regression. RESULTS: Out of the total number of women who participated in the study, 38.2% were not covered by health insurance. With maternal healthcare utilization, 12.5%, 23.2%, and 10.1% respectively, failed to make early first antenatal care visit, complete four or more antenatal care visits and have their delivery attended by a skilled worker. After controlling for the socio-demographic factors, health insurance coverage was associated with increased odds of early timing of first antenatal care visits and completion of four or more antenatal care visits (aOR = 1.33, p < 0.05, aOR = 1.25, p < 0.01, respectively). However, women who were covered by health insurance were less likely to use skilled birth attendance during delivery (aOR = 0.72 p < 0.001). CONCLUSIONS: Jordanian women with health insurance coverage were more likely to have early first antenatal care visits and complete four or more antenatal care visits. However, they were less likely to have their delivery attended by a skilled professional. This study provides evidence that health insurance coverage has contributed to increased maternal healthcare services utilization, only in terms of number and timing of antenatal care visits in Jordan. It is recommended that policy makers in Jordan should strengthen the coverage of health insurance in the country, especially among women of reproductive age in order to enhance the use of maternal healthcare services in the country.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".