Assessment of modern contraceptives continuation, switching and discontinuation among clients in Pakistan: study protocol of 24-months post family planning voucher intervention follow up
Bibliographic record
Abstract
BACKGROUND: Pakistan has the second highest fertility rate in South Asia and its increasing population growth presents a significant challenge for country's path to progress and development. Modern contraceptive methods only account for a slow-rising 26% of use in Pakistan which is further lowest in the underserved areas (< 20%), with a high unmet need for family planning (20%). The David and Lucile Packard Foundation USA and Pakistan funded two operational research projects from 2012 to 2015, that employed a Demand-side Financing (DSF) approach testing the effectiveness of single and multi-purpose voucher schemes in increasing access and uptake of FP services and products among the women of two-lowest income quintiles in the Punjab province of Pakistan. The present paper presents a study protocol which intends to assess the longer term impact of these two voucher intervention programs among married women of reproductive age (MWRA) who received contraceptive services through vouchers. METHODS: This will be a mixed methods study using qualitative and quantitative approaches. A quantitative cross sectional survey will measure the contraceptive uptake among voucher users, included in the endline survey and to examine the attitudes and behaviour of women with respect to contraceptive continuation, switching and discontinuation 24 months post intervention in two districts of Chakwal and Faisalabad in Punjab province of Pakistan. Qualitative in-depth interviews will be conducted with FP service providers operating in intervention areas and with key policy makers in the public sector to examine and document the service provider perspective on sustainability on contraceptive practices and behaviour in the post project closure period within the intervention areas. DISCUSSION: Globally, there is almost negligible direct evidence on the assessment of longer-term impact of a demand-side financing programs using free or subsidized vouchers for family planning services especially during post-intervention period or when donor money runs out. The findings of this study will help fill the knowledge gap in the context of sustainability issues post-intervention and will provide information to policy makers to develop and plan contraceptive services in the target area to sustain the positive behaviour change in the population.
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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 | 0.020 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 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".