Impacts of inequalities in utilization of key maternal health service on fertility preference among high parity women in four selected regions of Ethiopia
Bibliographic record
Abstract
BACKGROUND: Fertility is one of the three main components which determines the size, structure, and composition of a population, and fertility preferences are important measures for forecasting fertility levels of a population. Therefore, this study aims to assess the impacts of the inequalities in the utilization of key maternal health services on fertility preference among high parity women in four selected regions (i.e., Afar, Beninshangul-Gumuz, Gambella and Somali) of Ethiopia. METHODS: The study used data collected for the 2016 Ethiopian Demographic and Health Surveys (EDHS) survey. It includes a total of 977 high parity women (104 urban and 873 rural) residing in the four regions of the study. Bivariate logistic regression was used to select the potential variables by using a cut off p-value < 0.2 and then the multivariable logistic regression analyses were performed to assess the association between the selected explanatory variables (i.e., control and exposure variables) and the outcome variable (i.e. fertility preference). Further, the study employed a Population Attributable Fractions (PAFs) to examine the population level impacts of the inequalities in maternal and child health service variables on fertility preference. RESULTS: The results showed that religion, household wealth index, place of delivery and contraceptive use were significantly associated with fertility preference. The results of the PAFs analysis indicated that contraceptive use had the biggest impact (80%), meaning that non-use of contraceptive increased fertility preference among women in the four regions. Similarly, out of health facility delivery increased fertility preference by 37%. CONCLUSION: The study findings underscore the critical factors influencing women's fertility preferences in the studied regions. Low utilization of maternal health services had an impact on fertility preference, accounting for more than half of the observed variations in fertility preferences. To address this issue, targeted interventions are recommended: enhancing access to maternal health services, strengthening fertility control programs, promoting the benefits of using these services, and actively addressing inequalities.
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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 | 0.002 |
| 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 | 0.001 |
| 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".