Childhood adversities and clinical symptomatology in first-episode psychosis
Bibliographic record
Abstract
Abstract In addition to severe traumatic experiences, milder, more common childhood adversities reflecting psychosocial burden may also be common in people with psychotic disorders and have an effect on symptomatology and functioning. We explored eleven negative childhood experiences and their influence on clinical symptoms among young adults with first-episode psychosis (FEP, n = 75) and matched population controls ( n = 51). Individuals with FEP reported more adversities than controls. Specifically serious conflicts within the family, bullying at school, maternal mental health problems, and one's own and parents’ serious illness during childhood were experienced by the patients more often than by controls. In the FEP group, the severity of adversity was associated with increased anxiety, manic, and obsessive-compulsive symptoms, but not with the severity of positive psychotic symptoms. Adversity produced a more pronounced effect on symptoms in male patients than in female patients. To conclude, in line with earlier studies of more chronic psychosis, a majority of the participants with FEP reported exposure to childhood adversities, with the FEP group reporting more adversities than controls. High levels of mood and anxiety symptoms in patients with FEP may be related to cumulative exposure to childhood adversities. This should be taken into account in the treatment for 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".