Vaccination against COVID-19 in a geographically dispersed and underserved population, challenges and solutions in access and distribution of vaccines
Bibliographic record
Abstract
Background As of July 2021, only 57% of Ecuador’s population had received the first vaccine dose against COVID-19. The national immunization campaign faced difficulties in reaching and providing vaccines to underserved population in remote areas. Methods The RISE project, funded by USAID and implemented by Jhpiego, aimed to develop an immunization strategy, that through the collaboration of an international non-profit organization, an academic institution and the public sector, could effectively support the national vaccination campaign of the Ecuadorian Ministry of Public Health. Results The program identified gaps in vaccination access and uptake, established specific strategies for targeted communities, analyzed official geographical information on vaccination coverage, ran micro-planning exercises at the local level, and adapted to new needs identified to ensure an effective vaccination uptake. From November 2021 up to May 2023, more than 1.8 million COVID-19 vaccine doses were administered to underserved populations living in geographically dispersed areas, in 18 provinces. Conclusions Employing data-targeted approaches and microplanning to identify underserved populations, strategic planning and collaboration between local governments, private sector, academic institution, and community leaders can substantially improve COVID-19 vaccines coverage and, thus, equity to vaccine access. The lessons learned might be useful to improve overall immunization service delivery.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".