Breaking Barriers to Healthcare Access: A Multilevel Analysis of Individual- and Community-Level Factors Affecting Women’s Access to Healthcare Services in Benin
Bibliographic record
Abstract
Background: In low-income countries such as Benin, most people have poor access to healthcare services. There is scarcity of evidence about barriers to accessing healthcare services in Benin. Therefore, we examined the magnitude of the problem of access to healthcare services and its associated factors. Methods: We utilized data from the 2017–2018 Benin Demographic and Health Survey (n = 15,928). We examined the associations between the demographic and socioeconomic characteristics of women using multilevel logistic regression. The outcome variable for the study was problem of access to healthcare service. Adjusted odds ratios (AORs) with 95% confidence intervals (95% CI) were estimated. Results: Overall, 60.4% of surveyed women had problems in accessing healthcare services. Partner’s education (AOR = 0.70; 95% CI; 0.55–0.89), economic status (AOR = 0.59; 95% CI; 0.47–0.73), marital status (AOR = 0.44; 95% CI; 0.39–0.51), and parity (AOR = 1.85; 95% CI; 1.45–2.35) were significant individual-level factors associated with problem of access to healthcare. Region (AOR = 5.24; 95% CI; 3.18–8.64) and community literacy level (AOR = 0.69; 95% CI; 0.51–0.94) were the main community-level risk factors. Conclusions: Enhancing husband education through adult education programs, economic empowerment of women, enhancing national education coverage, and providing priority for unmarried and multipara women need to be considered. Additionally, there is the need to ensure equity-based access to healthcare services across regions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".