Adverse childhood experiences, social functioning deficits and comorbid clinical symptoms among individuals with psychotic disorders: A prospective cohort study
Bibliographic record
Abstract
Adverse Childhood Experiences (ACEs; e.g., sexual and physical abuse, exposure to intimate partner violence) are significant risk factors for developing psychosis and persistent psychotic disorders. Limited research has explored the influence of ACEs on the social functioning and comorbid clinical symptoms (depression, anxiety, and substance misuse) of individuals with psychotic disorders (PD) throughout their illness. This prospective cohort study aimed to determine whether the social functioning deficits and comorbid clinical symptoms of people with psychotic disorders varied over one year after psychiatric hospital admission, depending on the presence or absence of ACEs. Between 2012 and 2020, data were gathered by the Signature Biobank of the Institut Universitaire en Santé Mentale de Montréal for individuals hospitalized for psychotic symptoms (N = 970). Self-reported measures were used at four measurement times. Findings from the mixed-effects model for repeated measures revealed lower global social functioning and higher scores of depression, anxiety, and substance misuse over time for the psychotic disorder group with ACEs compared to the psychotic disorder group without ACEs. Implications for psychosis treatment with trauma-informed adaptations are discussed.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".