Communication strategies to promote the uptake of childhood vaccination in Nigeria: a systematic map
Bibliographic record
Abstract
BACKGROUND: Effective communication is a critical component in ensuring that children are fully vaccinated. Although numerous communication interventions have been proposed and implemented in various parts of Nigeria, the range of communication strategies used has not yet been mapped systematically. This study forms part of the 'Communicate to vaccinate' (COMMVAC) project, an initiative aimed at building research evidence for improving communication with parents and communities about childhood vaccinations in low- and middle-income countries. OBJECTIVE: This study aims to: 1) identify the communication strategies used in two states in Nigeria; 2) map these strategies against the existing COMMVAC taxonomy, a global taxonomy of vaccination communication interventions; 3) create a specific Nigerian country map of interventions organised by purpose and target; and 4) analyse gaps between the COMMVAC taxonomy and the Nigerian map. DESIGN: We conducted the study in two Nigerian states: Bauchi State in Northern Nigeria and Cross River State in Southern Nigeria. We identified vaccination communication interventions through interviews carried out among purposively selected stakeholders in the health services and relevant agencies involved in vaccination information delivery; through observations and through relevant documents. We used the COMMVAC taxonomy to organise the interventions we identified based on the intended purpose of the communication and the group to which the intervention was targeted. RESULTS: The Nigerian map revealed that most of the communication strategies identified aimed to inform and educate and remind or recall. Few aimed to teach skills, enhance community ownership, and enable communication. We did not identify any intervention that aimed to provide support or facilitate decision-making. Many interventions had more than one purpose. The main targets for most interventions were caregivers and community members, with few interventions directed at health workers. Most interventions identified were used in the context of campaigns rather than routine immunisation programmes. CONCLUSIONS: The identification and development of the Nigerian vaccination communication interventions map could assist programme managers to identify gaps in vaccination communication. The map may be a useful tool as part of efforts to address vaccine hesitancy and improve vaccination coverage in Nigeria and similar settings.
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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 | 0.026 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.023 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".