Eradicating polio in Pakistan: a systematic review of programs and policies
Bibliographic record
Abstract
Introduction: Established in 1994, Pakistan’s polio program demonstrated early success. However, despite over 120 supplementary immunization activities in the last decade, polio eradication efforts in Pakistan have been unable to achieve their objective of halting polio transmission. Variable governance, and inconsistent leadership and accountability have hindered the success of the polio program and the quality of the campaigns. Insecurity and terrorism has interrupted polio activities, and community fears and misbeliefs about polio vaccinations continue to persist.Areas covered: The article consists of a systematic review of the barriers and facilitators associated with the delivery of polio eradication activities in Pakistan. We also provide a comprehensive review of the policy and programmatic decisions made by the Pakistan Polio Programme since 1994. Searches were conducted on Embase and Medline databases and 25 gray literature sources.Expert opinion: Polio eradication efforts must be integrated with other preventive health services, particularly immunization services. Addressing the underlying causes of polio refusals including underdevelopment and social exclusion will help counteract resistance to polio vaccination. Achieving polio eradication will require building health systems that provide comprehensive community-centered care, and improving governance and systems of accountability.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".