Prevalence and determinants of contraception use in Pakistan: trend analysis from the Pakistan Demographic and Health Surveys (PDHS) dataset from 1990 to 2018
Bibliographic record
Abstract
<ns4:p> <ns4:bold>Background:</ns4:bold> In developing countries, pregnancy and childbirth are the leading causes of death among women. In this context, family planning and access to contraceptives are crucial for reducing pregnancy-related morbidity and mortality. Therefore, we aimed to look into the trends of contraception and determinants of contraceptive use in Pakistan. </ns4:p> <ns4:p> <ns4:bold>Methods:</ns4:bold> This study used data for women of reproductive age from four Pakistan Demographic and Health Surveys datasets. Contraception was the outcome variable, whereas, women’s and partner’s education, occupation, wealth quintile, region, place of residence, and exposure to family planning messages were the explanatory variables. Pooled prevalence was estimated using SUMARI and regression analysis was undertaken using SPSS to produce an adjusted prevalence ratio with 95% confidence intervals. </ns4:p> <ns4:p> <ns4:bold>Results:</ns4:bold> Data of 40,259 ever-married women of reproductive age (EMWRA) was analysed. Of the total EMWRA, 30% were using contraception. Of these, 26% were using traditional methods and 74% were using modern methods. The most common method of contraception was condoms (30.5%). The pooled prevalence of contraception used was 29.5% (95% CI 29.1 to 30.0). Through multivariate analysis, women's age, place of residence, region, wealth index, women’s education, their working status, and exposure to family planning messages were found to be significant determinants of contraception usage. </ns4:p> <ns4:p> <ns4:bold>Conclusions:</ns4:bold> There is a noticeable gap regarding awareness and uptake of contraception leading to low contraceptive use among women in Pakistan. In the light of our results, it is important to highlight the importance of girl’s education for building awareness and empowerment. </ns4:p>
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".