Subjective distress in first‐episode psychosis: role of symptoms and self‐esteem
Bibliographic record
Abstract
Abstract Background: Patients with psychotic disorders are assumed to experience significant distress, especially during their first episode. It is unclear whether such distress is associated with the level of psychotic or other symptoms and/or to other characteristics such as level of self‐esteem. Methods: One hundred and five patients who presented with first episode psychosis (FEP) (54% male; mean age 22.74 years; 79.4% schizophrenia; 20.6% affective psychosis) were administered the Symptom Checklist 90‐Revised (SCL‐90‐R) at first presentation for treatment. Four indices derived from the SCL‐90‐R were used as measures of distress. Psychopathology was assessed with the Calgary Depression Scale, the Hamilton Anxiety Scale, the Scale for the Assessment of Positive Symptoms and the Scale for the Assessment of Negative Symptoms and self‐esteem with the Self‐esteem Rating Scale.Spearman's Correlation coefficients were calculated, followed by a regression analysis. Results: Measures of distress were highly correlated with depression (rho = 0.44–0.56), and anxiety (rho = 0.38–0.48), modestly with lack of judgement and insight (rho = −0.28 to −0.37) and not with positive or negative symptoms of psychosis. In a smaller sample (n = 68) distress measures were also highly correlated with self‐esteem (rho = −0.55 to −0.73). Logistic regression confirmed that self‐esteem explained 53% of the total variance explained (57%) by any combination of the independent variables. Conclusion: Distress experienced by individuals suffering from FEP is associated with levels of self‐esteem, depression and anxiety and not with the level of psychotic or negative symptoms. Self‐esteem may play a significant role in the magnitude of distress experienced by patients presenting with a 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 | 0.001 | 0.010 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".