Abuse, Depressive Symptoms, Executive Functioning, and Overgeneral Memory Among a Psychiatric Sample of Children and Adolescents
Bibliographic record
Abstract
Prior research has established the independent associations of depressive symptoms and childhood trauma to overgeneral memory (OGM); the present study addresses the potentially interactive effects between these two risk factors on OGM. In addition, the current study comprehensively evaluates whether executive functions (EF) mediate the relation between depressive symptoms and/or abuse to OGM in a child and adolescent sample. OGM was assessed among an inpatient-psychiatric sample of 49 youth (ages 7-17) with, and without, child abuse histories and depressive symptomatology. EF was assessed with standardized neuropsychological measures of verbal fluency, inhibition, and cognitive flexibility. There was a significant interaction of depressive symptoms and abuse in predicting OGM; the effect of depression on OGM was less pronounced among youth with abuse histories, who had elevated OGM at both low and high depressive symptoms relative to those with no abuse and low depressive symptoms. Among the EF measures, only category fluency was associated with OGM. An additive, rather than mediational, model was supported, whereby category fluency accounted for a significant proportion of variance in OGM above child abuse and depressive symptoms. The meaning of these findings for models of OGM and clinical practice are emphasized.
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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 | 0.001 |
| 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".