Effects of Integrating Family Planning With Maternal, Newborn, and Child Health Services on Uptake of Voluntary Modern Contraceptive Methods in Rural Pakistan: Protocol for a Quasi-experimental Study
Bibliographic record
Abstract
BACKGROUND: The uptake of modern contraceptive methods (MCMs) remains low, with 25% of women reporting their use in Pakistan. The overarching interventions covering service delivery platforms at facility and community levels necessitate the integration of family planning (FP) with maternal, newborn, and child health (MNCH) services. OBJECTIVE: The main aim of this study is to evaluate the impact of an integrated FP-MNCH service delivery model to increase coverage of MCMs in rural Pakistan. Moreover, we aim to measure the level of effectiveness of interventions regarding the uptake of MCMs. METHODS: A quasi-experimental, sequential, mixed methods study design with pre- and postevaluation will be adopted to evaluate the impact of integration of FP with MNCH services. The interventions include the following: (1) capacity strengthening of health care providers, including technical trainings; training in counseling of women who attend immunization centers, antenatal care (ANC) clinics, and postnatal care (PNC) clinics; and provision of job aids; (2) counseling of women and girls attending ANC, PNC, and pediatric clinics; (3) ensuring sustained provision of supplies and commodities; (4) community engagement, including establishing adolescent-friendly spaces; and (5) use of District Health Information System data in decision-making. Descriptive statistics will be used to estimate prevalence (ie, proportions) and frequencies of outcome indicators. A univariate difference-in-difference analytical approach will be used to estimate the effect of the interventions. In addition, a Blinder-Oaxaca decomposition analysis will be conducted to identify and quantify determinants of the modern contraceptive prevalence rate. RESULTS: The intervention phase began in July 2021 and will run until June 2022. The impact assessment will be conducted from July to September 2022. CONCLUSIONS: This project will evaluate the impact of integrating FP with MNCH services. Furthermore, this study will identify the drivers and barriers in uptake of MCMs and will simultaneously help in modifying the interventional strategies that can be scaled up through existing service delivery platforms within the public and private sectors, according to the local sociocultural and health system context. TRIAL REGISTRATION: ClinicalTrials.gov NCT05045599; https://clinicaltrials.gov/ct2/show/NCT05045599. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/35291.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.033 | 0.020 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.041 | 0.005 |
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 source (direct Gemma or distilled Codex), 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".