Dietary diversity and its associated factors among adolescent girls in Ethiopia: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Even though fragmented and inconsistent findings have been reported in Ethiopia, adolescence is a period of rapid growth following infancy and is severely affected by micronutrient deficiencies, food insecurity, and poor-quality diets. Therefore, the aim of this meta-analysis was to estimate the pooled prevalence of dietary diversity and its associated factors among adolescent girls in Ethiopia. METHODS: International databases such as EMBASE, Hinari, Scopus, PubMed, Google Scholar, and direct Google searches were systematically used to search for articles and reports. The Newcastle-Ottawa Scale, modified as appropriate, was used for cross-sectional studies to assess the quality of the included articles and reports. A Microsoft Excel sheet was used for data extraction and then exported into STATA version 17 for further analysis. The pooled prevalence of dietary diversity was estimated using a random effects meta-analysis approach. Egger's and Begg's tests were employed to evaluate publication bias. RESULTS: = 99.2%, p = 0.00). Urban residence (OR: 2.46), mother being a government employee (OR: 2.31), attending a private school (OR: 6.24), adolescent having formal maternal education (OR: 4.49), adolescent having formal paternal education (OR: 3.26), father being a government employee (OR: 3.50), father being a merchant employee (OR: 2.51), middle family wealth index (OR: 1.76), household food security (OR: 3.96), receiving nutrition counseling (OR: 2.46), and higher meal frequency (OR: 7.35) were significantly associated with minimum dietary diversity. CONCLUSION: The pooled prevalence of dietary diversity among adolescent girls was low. Factors significantly associated with achieving minimum dietary diversity included urban residence, higher parental education and employment in government, private school attendance, household wealth, food security, receiving nutrition counseling, and higher meal frequency. These findings emphasize the need to improve dietary diversity among rural adolescent girls and food insecure households. Establishing nutrition counseling services could enhance understanding and skills related to a varied diet.
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
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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.032 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".