P-546 - Examining eating problems in aboriginal and nonaboriginal perinatal women
Bibliographic record
Abstract
Perinatal women undergo changes in their weight and shape, eating problems such as dieting, binge eating, and vomiting/purging may be expected to further stress the pregnant woman or contribute to perinatal anxiety and depression. The limited reports of studies comparing Aboriginal and nonAboriginal women have shown little difference in eating disorders between the two groups. In a longitudinal study of depression and its correlates in socially high-risk, perinatal women, we also collected data about diet, binge and vomiting/purge behaviours. We were interested in understanding more about these eating problems and their association with perinatal health. Descriptive, bivariate and regression analyses were done. Data were available on 283 women, 60% of whom were Aboriginal. The average age was 22.4 years and gestation was 15.4 weeks at onset of the study. Most women (84%) stated that they had no history of eating problems. Women who engaged in one eating problem behavior were significantly more likely to engage in another. NonAboriginal women were significantly more likely to report dieting, binging, or vomiting/purging (p < 0.0001). Overall, eating problems were associated with depression, mood swings, history of physical and sexual abuse, stress and recreational drug use. Women with eating problems were more likely to have gestational hypertension than those without, but confirming the literature, there were no other differences in pregnancy or birth outcomes. NonAboriginal perinatal women experience significantly more eating problems, but Aboriginal women with a history of depression, abuse, stress and drug use are at increased risk for eating problems.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".