Severe childhood trauma and emotion recognition in males and females with first‐episode psychosis
Bibliographic record
Abstract
Abstract Aim Childhood trauma increases social functioning deficits in first‐episode psychosis (FEP) and is negatively associated with higher‐order social cognitive processes such as emotion recognition (ER). We investigated the relationship between childhood trauma severity and ER capacity, and explored sex as a potential factor given sex differences in childhood trauma exposure. Methods Eighty‐three FEP participants (52 males, 31 females) and 69 nonclinical controls (49 males, 20 females) completed the CogState Research Battery. FEP participants completed the Childhood Trauma Questionnaire. A sex × group (FEP, controls) ANOVA examined ER differences and was followed by two‐way ANCOVAs investigating sex and childhood trauma severity (none, low, moderate, and severe) on ER and global cognition in FEP. Results FEP participants had significantly lower ER scores than controls (p = .035). No significant sex × group interaction emerged for ER F(3, 147) = .496, p = .438 [95% CI = −1.20–0.57], partial η2 = .003. When controlling for age at psychosis onset, a significant interaction emerged in FEP between sex and childhood trauma severity F(3, 71) = 3.173, p = .029, partial η2 = .118. Males (n = 9) with severe trauma showed ER deficits compared to females (n = 8) (p = .011 [95% CI = −2.90 to −0.39]). No significant interaction was observed for global cognition F(3, 69) = 2.410, p = .074, partial η2 = .095. Conclusions These preliminary findings provide support for longitudinal investigations examining whether trauma severity differentially impacts ER in males and females with FEP.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".