A meta‐analysis of the worldwide prevalence of pica during pregnancy and the postpartum period
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it