Overcoming vaccine deployment challenges among the hardest to reach: lessons from polio elimination in India
Bibliographic record
Abstract
After more than 30 years of efforts to eliminate polio, India was certified polio free by WHO in 2014. The final years prior to polio elimination were characterised by concentrated efforts to vaccinate hard-to-reach groups in the state of Uttar Pradesh, including migrant workers, religious minority Muslims and impoverished communities with poor pre-existing social support systems. This article aims to describe the management strategies employed by India to improve the deployment and acceptance of vaccines among hard-to-reach groups in Uttar Pradesh in the final years prior to polio elimination.Three main management principles contributed to polio elimination among the hardest to reach in Uttar Pradesh: bundling of health services, local stakeholder engagement and accountability mechanisms for public health initiatives. In an effort to market the polio campaign as an authentic health-oriented programme, vaccine acceptance was improved by packaging other basic healthcare services such as routine check-ups and essential medications. India also prioritised local stakeholder engagement by using influential community leaders to reach vaccine hesitant groups. Lastly, the accountability mechanisms developed between non-profit organisations and decision-makers in the field ensured accurate reporting and identified deficiencies in healthcare worker training. The lessons learnt from India's polio vaccination programme have important implications for the implementation of future mass vaccination initiatives, particularly when trying to reach vulnerable communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".