Association between early childhood caries and intimate partner violence in 20 low- and middle-income countries: 2007-2017
Bibliographic record
Abstract
Abstract Background: To determine the relationship between country level prevalence of interpersonal violence (IPV) and the prevalence of early childhood caries (ECC) in children aged 3-5-year-olds. Method: This was an ecological study using extracted IPV (physical, sexual and emotional) and ECC data for 3-5-year-olds in 20 low- and middle-income countries for the period 2007-2017. Linear regression analysis was used to assess the relationship between the percentage of 3-5-year-old children with ECC (outcome variable) and the four IPV indicators (physical, sexual, emotional and a combination of the three). The model was adjusted for the country’s Gross National Income GNI. Partial eta squared (as measure of effect size), regression coefficients, confidence intervals and p values were calculated. Results: The strongest association was between ECC prevalence and exposure to physical violence (partial eta squared= 0.01), followed by exposure to sexual violence (partial eta squared= 0.005), and exposure to all types of IPV combined (partial eta squared= 0.001). Exposure to emotional violence had the weakest association with ECC (partial eta squared < 0.0001). For 1% higher percentage of women reporting exposure to physical violence and percentage of women reporting all types of IPV combined, there was a 0.18% higher prevalence of ECC. For 1% higher prevalence of sexual violence, there was 0.22% higher ECC prevalence. For 1% higher prevalence of emotional violence, there was 0.04% higher ECC prevalence. Conclusions: Countries with high prevalence of IPV will likely also have high prevalence of ECC. This needs further studies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".