Acceptability of mobile-phone reminders for routine childhood vaccination appointments in Nigeria – a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Mobile-phone reminders have gained traction among policymakers as a way to improve childhood vaccination coverage and timeliness. However, there is limited evidence on the acceptability of mobile-phone reminders among patients and caregivers. This systematic review and meta-analysis aimed to evaluate the ownership of mobile-phone device and the willingness to receive mobile-phone reminders among mothers/caregivers utilizing routine childhood immunization services in Nigeria. Method MEDLINE, Scopus, CINAHL, CNKI, AJOL (African Journal Online), and Web of Science were systematically searched for studies on the acceptability of mobile-phone reminders for routine immunization appointments among mothers/caregivers in Nigeria. Studies were assessed for methodological quality using the Newcastle Ottawa Scale and JBI critical appraisal checklists. Meta-analysis was conducted using random-effects model to generate pooled estimates (proportion) of mothers who owned at least one mobile phone and proportion of mothers willing to receive mobile-phone reminders. Results Sixteen studies (13 cross-sectional and three interventional) involving a total of 9923 mothers across 15 states and the Federal Capital Territory Abuja met inclusion criteria. Pooled estimates showed that the proportion of mothers who owned at least one mobile phone was 96.4% (95% CI = 94.1–98.2%; I2 = 96.3%) while the proportion of mothers willing to receive mobile-phone reminders was 86.0% (95% CI = 79.8–91.3%, I2 = 98.4%). Most mothers preferred to receive text message reminders at least 24 h before the routine immunization appointment day, and in the morning hours. Approximately 52.8% of the mothers preferred to receive reminders in English, the country’s official language. Conclusion Current evidence suggests a high acceptability for mobile-phone reminder interventions to improve routine childhood immunization coverage and timeliness. Further studies, however, are needed to better understand unique regional preferences and assess the operational costs, long-term effects, and risks of this intervention. Systematic review protocol registration PROSPERO CRD42021234183.
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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 | 0.020 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".