Are family planning vouchers effective in increasing use, improving equity and reaching the underserved? An evaluation of a voucher program in Pakistan
Bibliographic record
Abstract
BACKGROUND: Low modern contraceptive prevalence rate and high unmet need in Pakistan aggravates the vulnerabilities of unintended pregnancies and births contributing to maternal morbidity and mortality. This research aims to assess the effectiveness of a free, single-purpose voucher approach in increasing the uptake, use and better targeting of modern contraceptives among women from the lowest two wealth quintiles in rural and urban communities of Punjab province, Pakistan. METHODS: A quasi-interventional study with pre- and post-phases was implemented across an intervention (Chakwal) and a control district (Bhakkar) in Punjab province (August 2012-January 2015). To detect a 15% increase in modern contraceptive prevalence rate compared to baseline, 1276 women were enrolled in each arm. Difference-in-Differences (DID) estimates are reported for key variables, and concentration curves and index are described for equity. RESULTS: Compared to baseline, awareness of contraceptives increased by 30 percentage points among population in the intervention area. Vouchers also resulted in a net increase of 16% points in current contraceptive use and 26% points in modern methods use. The underserved population demonstrated better knowledge and utilized the modern methods more than their affluent counterparts. Intervention area also reported a low method-specific discontinuation (13.7%) and high method-specific switching rates (46.6%) amongst modern contraceptive users during the past 24 months. The concentration index indicated that voucher use was more common among the poor and vouchers seem to reduce the inequality in access to modern methods across wealth quintiles. CONCLUSION: Vouchers can substantially expand contraceptive access and choice among the underserved populations. Vouchers are a good financing tool to improve equity, increase access, and quality of services for the underserved thus contributing towards achieving universal health coverage targets.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".