Experiences, perceptions and preferences of mothers towards childhood immunization reminder/recall in Ibadan, Nigeria: a cross-sectional study
Bibliographic record
Abstract
INTRODUCTION: Immunization reminder/recall system is proven as one of the effective ways of improving immunization rates. Prior to the development and implementation of an immunization reminder/recall system intervention, we explored the experiences, preferences and perceptions towards childhood immunization reminder/recall among 614 mothers of infants in Ibadan, Nigeria. METHODS: A cross-sectional health facility-based survey utilizing a semi-structured questionnaire was conducted in four Primary Health Care centers. Descriptive statistics were computed using SPSS. Logistic models were used to investigate the relationships with specific outcomes. RESULTS: Only 3.9% had ever heard of immunization reminder/recall and 1.5% had ever received one. However, 97.9% were willing to record their cellphone numbers in the clinics for immunization reminder/recall and 95.1% were willing to receive. Their preferred communication modes were cell phone calls (57.6%) or text messages/SMS (35.6%). Only 2.2% preferred home-visits and 0.4%, e-mails. About 4% were not willing to receive any form of immunization reminder/recall. Mothers with post-secondary education were more likely to prefer SMS than other mothers (OR 2.3, 95% CI 1.7-3.3, p. CONCLUSION: This study provided critical baseline data for designing a reminder/recall intervention for routine childhood immunization in the study communities. The findings may serve as a guide for public health professionals in designing reminder/recall strategies to improve childhood immunization.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".