Improving Immunization utilization using participatory action research in Nigeria
Bibliographic record
Abstract
Background In 2005, Nigeria adopted the Reach Every Ward (REW) strategy to improve vaccination coverage for children, 0–23 months. By 2015, Ogun state had full coverage in 12 of its 20 local government areas but eight had pockets of unimmunized children, with the highest burden (37%) in Remo-North. Since the exact factors responsible for this trend were not known, participatory action research (PAR) was introduced. Through iterative processes of reflection and action, among communities, health workers and local government officials, insights into the relevant problems identified by different groups in the community were discussed and as their realistic, context-specific solutions designed and implemented. However, a knowledge gap was that there was little known about whether and how PAR would lead to improved access to immunization services and change of immunization-seeking behaviour in communities in Nigeria. This thesis aims to gain insight into if and how PAR can be used to develop context-specific strategies to improve access to and utilization of immunization in Nigeria. Methods The PAR intervention took place from 2016 to 2017. It involved two (4-month) cycles of dialogue and action between community members, frontline health workers and local government officials in two wards of Remo North, facilitated by the research team. The PAR involved outcome and process assessments which included four studies (two qualitative and two mixed methods studies). Data was analysed using the Strategic Advisory Group of Experts (SAGE) vaccine hesitancy framework. Results The studies showed that immunization utilization and access are influenced by interlinked community and health services issues. Involving community, health service and policy actors in the PAR is critical to addressing immunization access and utilisation challenges and ensuring that strategies are adjusted to suit the contexts. Integration of evidence into dialogues with stakeholders can lead to change, while leveraging existing government and community structures and resources enhances effectiveness of strategies. The PAR approach enables development of effective partnerships amongst government, health workers and communities to achieve health-related goals and to put the needs of the community at the center in development of solutions, even in the presence of asymmetries in relationships. However, intra-community dynamics and socio-cultural contexts drive exclusion of vulnerable groups. Effectively addressing issues of less privileged community members requires inclusion strategies for proper representation of these groups. Having a valorisation strategy is crucial for encouraging government ownership and enhancing the utilisation of research results to (sustainably) improve immunization issues locally and at state level. Conclusion PAR has the potential to actively involve affected communities in the decision-making processes that impact their health and immunization and to facilitate co-creation of contextualised solutions (with health workers and local governments) to address community needs and enhance sustainable change. At the same time, PAR cannot change major flaws in the system, such as governance arrangements, or solve contextual elements such as turnover of health workers or commitment of policy makers. In addition, inclusion of vulnerable groups requires specific strategies.
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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 | 0.049 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".