A prospective study of effects of prenatal maternal stress on later eating‐disorder manifestations in affected offspring: Preliminary indications based on the project ice storm cohort
Bibliographic record
Abstract
BACKGROUND: Research associates maternal stress exposures (especially when occurring late in gestation) with heightened risk of subsequent emotional and behavioral problems in affected offspring. However, as yet, no study has examined the association between prenatal maternal stress (PNMS) and affected children's risk of anorexia- or bulimia-type eating disturbances. OBJECTIVE: To study the influences of PNMS on later disordered eating in exposed offspring. METHOD: We used the Eating Attitudes Test (EAT)-26 to measure eating attitudes and behaviors in 54 thirteen-year olds whose mothers had been exposed, while pregnant with these children, to the 1998 Quebec Ice Storm-a natural disaster regarded as a model of exposure to severe environmental stress. Mothers' stress was measured shortly after exposure to the storm using established indices of objective and subjective stress. RESULTS: Hierarchical multiple linear regression analyses indicated that once variance owing to children's body mass index and sex was accounted for, stress exposures during the third trimester of pregnancy predicted elevated EAT-26 scores in affected children-perhaps even more so when levels of objective stress were high. DISCUSSION: Third trimester exposure to PNMS, especially when objectively severe, seems to be associated with increased eating-disorder-linked manifestations in affected early adolescents.
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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".