Contraceptive Use among Women with Chronic Medical Conditions and Factors Associated with Its Non-Use in Malaysia
Bibliographic record
Abstract
INTRODUCTION: Women with chronic medical conditions are at higher risk of adverse pregnancy outcomes, which may be minimized through optimal preconception care and appropriate contraceptive use. This study aimed to describe contraceptive use among women with chronic medical conditions and factors associated with its non-use. METHODS: This study used cross-sectional data from a family planning survey among women with chronic medical conditions conducted in three health facilities in a southern state of Malaysia. A total of 450 married women in reproductive age (18-50 year) with intact uterus, and do not plan to conceive were analysed for contraceptive use. Both univariate and multivariate analysis was conducted to identify factors associated with contraceptive non-use among the study participants. RESULTS: A total of 312 (69.3%) of the study participants did not use contraceptive. Contraceptive non-use was highest among the diabetics (71.2%), connective tissue disease patients (68.6%) and hypertensive patients (65.3%). Only 26.3% of women with heart disease did not use contraceptive. In the multivariate analysis, contraceptive non-use was significantly more common among women who received their medical treatment in the health clinics as compared to those who received treatment in the hospital (adjusted odds ratio [OR]=1.75, 95% confidence interval [CI]: 1.09, 2.79), being in older age group of 41-50 year (adjusted OR=2.31, 95% CI: 1.19, 4.48), having children (adjusted OR=4.57, 95% CI: 1.66, 12.57) and having lower education (adjusted OR=2.87, 95% CI: 1.43, 5.77). CONCLUSION: About two-third of women with chronic medical conditions who needed contraceptive did not use them despite the higher risk of pregnancy related complications. The high unmet need warrant an effective health promotion programme to encourage the uptake of contraceptives especially targeting women of older age group, low education and those who received their medical treatment at health clinics.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| 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".