Urban‒rural differences and determinants of utilization of sexual and reproductive health services among males in Anambra State, South-eastern Nigeria
Bibliographic record
Abstract
INTRODUCTION: Males have substantial sexual and reproductive health (SRH) needs, but there is low utilization of these SRH services. We compared the utilization and ascertained factors associated with the utilization of sexual and reproductive health services among males in urban and rural areas of Anambra State, Nigeria. METHODS: A community-based cross-sectional study was conducted in Anambra State. Using multistage sampling, 1147 respondents were surveyed via interviewer-administered, structured questionnaires. Data was collected on socio-demographic characteristics, utilization of, and factors associated with the utilization of SRH services. We compared differences in the utilization of SRH services among males using Z test. Simple binary logistic regression analysis was performed to obtain odds ratios and 95% confidence intervals of the crude associations between each predisposing, enabling, and need factor and the utilization of male SRH services. Factors that had a p-value of ≤ 0.05 in the bivariable analysis were included in a multivariable logistic regression analysis to ascertain the independent association between each factor and the utilization of SRH services while controlling for the other factors. The level of statistical significance was set at < 0.05. RESULTS: The mean age of the respondents was 28.00 ± 9.35 years. There was a low level of utilization, with males in rural areas (35.6%) having a higher level of utilization than males in urban areas (27.0%). Rural location, older age, higher educational level, communication with sexual partner, access to SRH information, exposure to SRH information on mass media, access to health insurance, being sexually active, feeling susceptible to SRH diseases, and having a diagnosis of SRH disease in the last 12 months were positively associated with the utilization of male sexual and reproductive health services. Marital and employment status, number of children, number of sexual partners and recent sexual intercourse were negatively associated with the utilization of male sexual and reproductive health services. CONCLUSION: There is suboptimal utilization of SRH services in Anambra State, Nigeria. Interventions targeting these identified factors could increase the utilization of male sexual and reproductive health services in such settings.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".