Increasing self-esteem: Efficacy of a group intervention for individuals with severe mental disorders
Bibliographic record
Abstract
BACKGROUND: Individuals with psychosis are known to have a lower self-esteem compared to the general population, in part because of social stigma, paternalistic care, long periods of institutionalization and negative family interactions. This study aimed at assessing the efficacy of a self-esteem enhancement program for individuals with severe mental illness and at analyzing the results in their European context. METHOD: A randomized cross-over study including 54 outpatients with a diagnosis of schizophrenia from Geneva, Switzerland, was conducted. Twenty-four were recruited from an outpatient facility receiving traditional psychiatric care whereas 30 came from an outpatient facility with case-management care. Psychosocial, diagnostic and symptom measures were taken for all the subjects before treatment, after treatment, and at 3-months' follow-up. RESULTS: Results indicated significant positive self-esteem module effects on self-esteem, self-assertion, active coping strategies and symptom for the participants receiving case-management care. Results were not significant for those receiving traditional care. However, 71% of all participants expressed satisfaction with the module. CONCLUSION: Individuals with schizophrenia appear to be benefit from the effects of the self-esteem module, particularly when they are involved in a rehabilitation program and followed by a case manager who liaises with the other partners of the multidisciplinary team. This encourages reconsidering the interventions' format and setting in order to ensure lasting effects on the environment and in turn on coping, self-esteem and overall empowerment.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".