Opportunities and barriers arising from the COVID-19 pandemic for health campaign integration across immunizations, neglected tropical diseases, insecticide-treated bed nets, and vitamin A supplementation: A qualitative key informant interview study
Bibliographic record
Abstract
In low- and middle-income countries, health campaigns play a crucial role in addressing high-priority health concerns such as neglected tropical diseases (NTDs), malaria, vaccine-preventable diseases and nutrition (vitamin A supplementation). Many health campaigns are conducted throughout the calendar year, resulting in multiple campaigns annually in some communities. Campaign integration offers an opportunity to increase efficiencies across programs and limit the strain on healthcare workers, communities, and health systems. The response to the COVID-19 pandemic provided opportunities for campaign integration to mitigate losses from missed campaign deliveries. As the interest in integration grows, there is a need to understand existing barriers, bottlenecks, and opportunities for better integration, whether through co-delivery or increased collaboration. This qualitative study aimed to understand the opportunities and barriers to campaign co-delivery and collaboration from the perspective of 26 stakeholders involved in vaccine-preventable diseases, vitamin A supplementation, NTDs, and malaria programs. Key informants included campaign managers, implementing partners, donors, and decision-makers at the country and state levels in five countries: Côte d'Ivoire, Ethiopia, Guyana, Indonesia, and Nigeria. Results indicated that campaigns were integrated at various levels, from partial integration and co-delivery to fully integrated delivery within existing health services. Most emerging factors were categorized as either facilitating or hindering campaign integration. Enablers to campaign integration included joint planning and appreciation for human resources, while target population variation, prioritization of one intervention by another, and overburdening of healthcare workers, community health workers and community drug distributors were identified as specific barriers. An emerging theme was the importance of leadership, reinforcing the need for country ownership, political will, and positive stakeholder relationships. Further research is warranted to identify optimal combinations of campaign commodities, strategies to ensure stakeholder engagement throughout the integration process, methods to factor in local community context, and the expansion of lessons learned from the COVID-19 pandemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".