Treatment Outcome in Psychiatric Inpatients: The Discriminative Value of Self-Esteem
Bibliographic record
Abstract
UNLABELLED: Self-esteem has been identified as an important clinical variable within various psychological and psychiatric conditions. Surprisingly, its prognostic and discriminative value in predicting treatment outcome has been understudied. OBJECTIVE: The current study aims to assess, in an acute psychiatric setting, the comparative role of self-esteem in predicting treatment outcome in depression, anxiety, and global symptom severity, while controlling for socio-demographic variables, pre-treatment symptom severity, and personality pathology. DESIGN: Treatment outcome was assessed with pre- and post-treatment measures. METHOD: A heterogeneous convenience sample of 63 psychiatric inpatients completed upon admission and discharge self-report measures of depression, anxiety, global symptom severity, and self-esteem. RESULTS: A significant one-way repeated-measures multivariate analysis of variance (MANOVA) followed up by analyses of variance (ANOVAs) revealed significant reductions in depression (eta2 = .72), anxiety (eta2 = .55), and overall psychological distress (eta2 = .60). Multiple regression analyses suggested that self-esteem was a significant predictor of short-term outcome in depression but not for anxiety or overall severity of psychiatric symptoms. The regression model predicting depression outcome explained 32% of the variance with only pre-treatment self-esteem contributing significantly to the prediction. CONCLUSIONS: The current study lends support to the importance of self-esteem as a pre-treatment patient variable predictive of psychiatric inpatient treatment outcome in relation with depressive symptomatology. Generalization to patient groups with specific diagnoses is limited due to the heterogeneous nature of the population sampled and the treatments provided. Implications for clinical practice and future research 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 | 0.006 |
| 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 | 0.000 |
| 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".