A meta‐analysis of the worldwide prevalence of pica during pregnancy and the postpartum period
Bibliographic record
Abstract
BACKGROUND: Although pica has long been associated with pregnancy, the exact prevalence in this population remains unknown. OBJECTIVES: To estimate the prevalence of pica during pregnancy and the postpartum period, and to explain variations in prevalence estimates by examining potential moderating variables. SEARCH STRATEGY: PsycARTICLES, PsycINFO, PubMed, and Google Scholar were searched from inception to February 2014 using the keywords pica, prevalence, and epidemiology. SELECTION CRITERIA: Articles estimating pica prevalence during pregnancy and/or the postpartum period using a self-report questionnaire or interview were included. DATA COLLECTION AND ANALYSIS: Study characteristics, pica prevalence, and eight potential moderating variables were recorded (parity, anemia, duration of pregnancy, mean maternal age, education, sampling method employed, region, and publication date). Random-effects models were employed. MAIN RESULTS: In total, 70 studies were included, producing an aggregate prevalence estimate of 27.8% (95% confidence interval 22.8-33.3). In light of substantial heterogeneity within the study model, the primary focus was identifying moderator variables. Pica prevalence was higher in Africa compared with elsewhere in the world, increased as the prevalence of anemia increased, and decreased as educational attainment increased. CONCLUSIONS: Geographical region, anemia, and education were found to moderate pica prevalence, partially explaining the heterogeneity in prevalence estimates across the literature.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.025 |
| 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 | 0.002 |
| 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".