Predictors of voluntary uptake of modern contraceptive methods in rural Sindh, Pakistan
Bibliographic record
Abstract
The use of modern contraceptive methods (MCMs) has been stagnant for the last decade in Pakistan. The second most populous province, Sindh reports 25% of MCMs use. Various factors including demographics and health services utilization are associated with the uptake of family planning services. This research aimed to identify and assess specific predictors of MCMs among women aged 15-49 in two districts of Sindh-Matiari and Badin. A cross-sectional household survey was conducted from October 2020- December 2020. In total, 1684 Married Women of Reproductive Age (MWRA) 15-49 years were interviewed. For the selection of eligible respondents, a two-stage stratified cluster sampling strategy was used. Univariate and multivariable logistic regression was used to determine the predictors for the use of MCM. Use of modern methods of contraceptive was 26.1% (n = 441). Statistically significant socio demographic predictors of MCM included: number of children 4 or more (AOR: 5.23; 95%CI: 2.78-9.84), mother having primary education (AOR: 1.73; 95% CI: 1.26-2.36), and husband having middle education (AOR: 1.69; 95% CI: 1.03-2.76). Maternal health services indicators included: postnatal care of mother (AOR: 1.46; 95% CI: 1.09-2.05); women who were visited by Lady Health Workers in their postnatal period and were counselled on family planning (AOR: 1.83; 95% CI: 1.38-2.42). Since the primary purpose of using modern contraceptive methods is for limiting pregnancies, there is a potential to promote awareness about the benefits of birth spacing as part of implementing a more integrated approach to family planning. The integration of family planning services within maternal and newborn child healthcare services effectively promote the voluntary adoption of modern contraceptive methods. The role of Lady Health Workers in family planning counseling and service provision and uptake is important in the context of Sindh and should be fostered further by opportunities for capacity building and their empowerment.
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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 | 0.000 |
| 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 | 0.000 |
| 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".