Disentangling adversity timing and type: Contrasting theories in the context of maternal prenatal physical and mental health using latent formative models
Bibliographic record
Abstract
Abstract Research on the effects of adversity has led to mounting interest in examining the differential impact of adversity as a function of its timing and type. The current study examines whether the effects of different types (i.e., physical, sexual, and emotional abuse) and timing (i.e., early, middle childhood, adolescence, or adulthood) of adversity on maternal mental and physical health outcomes in pregnancy, are best accounted for by a cumulative model or independent effects model. Women from a prospective pregnancy cohort (N =3,362) reported retrospectively on their experiences of adversity (i.e., physical, sexual, and emotional abuse) in early childhood (0–5 years], middle childhood (6–12 years], adolescence (13–18 years], and adulthood (19+ years]. Measures of overall health, stress, anxiety, and depression were gathered in pregnancy. Results showed that a cumulative formative latent model was selected as more parsimonious than a direct effects model. Results also supported a model where the strength of the effect of adversity did not vary across abuse timing or type. Thus, cumulative adversity resulted in greater physical and mental health difficulties. In conclusion, cumulative adversity is a more parsimonious predictor of maternal physical and mental health outcomes than adversity at any one specific adversity timing or subtype.
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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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".