The Last Mile—Community Engagement and Conditional Incentives to Accelerate Polio Eradication in Pakistan: Study Protocol for a Quasi-Experimental Trial
Bibliographic record
Abstract
Poliomyelitis is a condition of great concern and is endemic in only two countries of the world: Pakistan and Afghanistan. Community mobilization plays a vital role in raising awareness and can help reduce polio vaccine refusals. The objective of this study will be to decrease polio vaccine refusals and zero-dose vaccines by motivating behavior change through the provision of conditional-collective-community-based incentives (C3Is) based on a reduction in polio vaccine refusals. The project will adopt a pretest/post-test quasi-experimental design with two intervention high-risk union councils (HRUCs) and two control union councils (UCs) of peri-urban (Karachi) and rural (Bannu) settings in Pakistan. A participatory community engagement and demand creation strategy with trust-building community mobilization with C3Is, to reduce vaccine refusals and improve polio immunization coverage in two HRUCs, will be used. These UCs will be divided into clusters based on the polio program framework and community groups will be formed in each cluster. These community groups will carry out awareness activities and will be given serial targets to reduce vaccine refusals and those who qualify will be provided C3Is. The project intends to create a replicable model that the government can integrate within health systems for long-term sustainability until the goal of eradication of poliovirus is achieved. The evaluation will be carried out by an independent data collection and analysis team at baseline and endline (after 12 months of intervention). The trial is registered with linicalTrials.gov with number NCT05721274.
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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 | 0.031 | 0.024 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.074 | 0.012 |
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".