Practice and factors associated with sunlight exposure of infants among mothers in Ethiopia: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Exposure to sunlight aids in the body's production of vitamin D, guards against rickets, and treats newborn jaundice. In Ethiopia, the magnitude of sunlight exposure practice varies across studies. Thus, this study aimed to estimate the pooled practices and factors associated with sunlight exposure of infants among mothers in Ethiopia. METHODS: Electronic search was carried out using databases (PubMed, HINARI, Science Direct, electronic databases, and Google Scholar) for relevant articles published from January 1, 2010, to March 27, 2024. The screening process was carried out in accordance with PRISMA guidelines. Articles conducted in English and quantitatively expressed were considered in this review. The quality assessment of included articles was evaluated using the Newcastle-Ottawa Scale. Data analysis was carried out using STATA-14 version software. I2 statistics and Egger's test were used to evaluate heterogeneity and publication bias, respectively. The pooled prevalence with a 95% confidence interval (CI) of the meta-analysis utilizing the random effect model was displayed using forest plots, and adjusted odds ratio (AOR) was utilized to quantify the association. RESULTS: 1171 records, 13 studies were included in the meta-analysis with 5190 study participants that fulfill the inclusion criteria, and all the included studies were cross-sectional in design. The pooled prevalence of sunlight exposure practice among mothers in Ethiopia was 45.38% CI (38.36, 52.4). Good sunlight-related knowledge (AOR; 1.61, 95% CI 1.3, 1.98), maternal formal education (AOR; 1.35, 95% CI 1.08, 1.69), housewife (AOR; 0.7, 95% CI 0.51, 0.95), and husband formal education (AOR; 1.29, 95% CI 1.07, 1.56) were significantly associated with good sunlight exposure practice. CONCLUSION: The pooled prevalence of good sunlight exposure practice among mothers in Ethiopia was low. Good sunlight-related knowledge, being a housewife, and maternal and husband formal education were the factors that were associated with good sunlight exposure practice. Thus, the government needs to cater further assistance and initiate greater information circulation and follow-up to improve the situation.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.032 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".