Impact of integrating family planning with maternal and child health on uptake of contraception: A quasi-experimental study in rural, Sindh, Pakistan
Bibliographic record
Abstract
This study aimed to evaluate the impact of integrating family planning with maternal and child health (FP-MCH) on the uptake of modern contraceptive methods (MCMs) and related health outcomes in two rural districts of Sindh, Pakistan using a quasi-experimental control before-after study design. Intervention package integrated FP with MCH in the existing public sector at facility and community levels. This included capacity building of health care providers, ensuring sustained supplies of family planning commodities, and community engagement activities through Lady Health Workers (LHW). Data was collected through household surveys at baseline (1st October- 31st December 2020) and endline (1st October- 31st December 2022). Sample size was calculated as 880 married women of reproductive age (MWRA) in each district. The Difference-in-Differences (DiD) analytical method was used to assess the impact of intervention. There was an increase of 11.7% in current use of MCMs in the intervention group compared to the control group (p-value <0.003), with increases observed in uptake of injections, implants, and condoms. Furthermore, family planning counseling during ANC (DiD 9.1%, p-value 0.162), LHW visits during pregnancy (DiD 15.4%, p-value 0.018), postnatal care (PNC) visits for mothers (DiD 24.0%, p-value <0.001), LHW visits after delivery (DiD 20.6%, p-value <0.001), and counseling by LHW about family planning at PNC visit (DiD 15.3%, p-value 0.027). The study concludes that integrating FP with MCH services proved impactful in increasing contraception uptake and minimizing missed opportunities. It underscores the necessity for cohesive efforts by the government and local stakeholders to design local, regional, and national policy frameworks pertaining to health and population planning for sustainable mother and child health improvements.
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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".