Modern contraceptive utilization and associated factors among married and cohabiting women in Papua New Guinea: a population-based cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Universal access to family planning has been emphasized by the international development agenda, as evident in the Sustainable Development Goal 3.7. This notwithstanding, the use of modern contraceptives has been minimal in low- and middle-income countries, especially in Papua New Guinea. In view of this, we investigated the factors associated with the use of modern contraceptives and the associated factors among married and cohabiting women in Papua New Guinea. METHODS: The study utilised the Demographic and Health Survey data of 2345 women in sexual unions in Papua New Guinea. We employed a descriptive and binary logistic regression analyses. We presented the results as crude Odds Ratios (COR) and adjusted Odds Ratios (AOR), with 95% confidence intervals (CI) signifying level of precision. Level of statistical significance was set at p < 0.05. RESULTS: We found that 74.4% of the women were using modern contraceptives ranging from injectables (44.5%) to other modern methods (0.23%). Women aged 15-19 [AOR = 7.425, 95% CI = 2.853, 19.32], residents of the Highland region [AOR = 1.521, 95% CI =1.086, 2.131], self-employed women in the agricultural sector [AOR = 1.710, 95% CI = 1.218, 2.400], and women who listened to radio at least once a week [AOR = 1.409, 95% CI = 1.048, 1.895] had higher odds of modern contraceptive usage. However, women in the Islands region [AOR = 0.291, 95% CI = 0.224, 0.377], women whose husbands had higher education [AOR = 0.531,95%CI = 0.318,0.886], women in professional/technical/managerial work [AOR = 0.643, 95% CI = 0.420, 0.986], and those with no child [AOR = 0.213, CI = 0.0498,0.911] had lower odds of modern contraceptive use. CONCLUSION: Out of the 2345 participants, we found that majority of them were using modern contraceptives and the commonly used modern contraceptive was injectables. Age, region of residence, partner's education, employment, partner's desire for children, and frequency of listening to radio are associated with modern contraceptive usage. Tailored reproductive healthcare should be developed for women who are disadvantaged when it comes to the usage of modern contraceptives in order to boost modern contraceptive use among them. Further investigation is needed to unravel the motivation for the high usage of injectables among married and cohabiting women in Papua New Guinea.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".