The role of self-esteem for outcome in first-episode psychosis
Bibliographic record
Abstract
BACKGROUND: Self-esteem may be associated with a wide range of psychiatric disorders, including psychotic disorders. However, the relationship between self-esteem and outcome in psychosis has not been adequately examined, especially early in the course of the illness. AIM: The aim of the study was to examine the impact of self-esteem in individuals who presented for treatment of a first episode of psychosis on outcome early in the course of the illness. METHODS: The Self-Esteem Rating Scale (SERS) was administered to 121 individuals with first-episode psychosis following entry into a specialized programme. Symptoms and the Global Assessment of Functioning (GAF) were assessed at six months after beginning treatment. A correlational analysis was followed by a linear regression controlling for potential confounds. RESULTS: Self-esteem assessed early in the course of treatment was positively correlated with GAF at six months (r = 0.281, p < 0.01). A linear regression analysis conducted with GAF and depression at baseline in addition to gender, pre-morbid adjustment, duration of untreated psychosis (DUP), and self-esteem as predictors and GAF at six months as the outcome variable revealed only self-esteem to be a significant predictor of GAF at six months (β = 0.290, p < 0.01). However, no association was found between self-esteem and remission at six months (β = 0.003, p > 0.05). CONCLUSION: Self-esteem is associated with global functional outcome at six months but not with remission of symptoms. Efforts should be made to provide interventions that may improve low self-esteem in the attempt to influence functional outcome.
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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 | 0.002 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".