Evaluation of self-esteem as a worker for people with severe mental disorders
Bibliographic record
Abstract
Self-esteem plays an important role in the recovery, particularly the work integration, of people with severe mental disorders. The Rosenberg Self-Esteem Scale, a widely used instrument that taps into global self-esteem, has been adapted to specifically assess self-esteem as a worker. The present study aimed at validating the Rosenberg Self-Esteem as a Worker Scale and determining its sensitivity to change in people with severe mental disorders registered in Supported Employment programs. An exploratory factor analysis showed two emerging factors later supported by a confirmatory factor analyses. The first subscale was named "Individual Self-Esteem as a Worker", and the second subscale, was entitled "Social Self-Esteem as a Worker". A subsequent MANCOVA further showed that the past work experience has a significant main effect on the Individual Self-Esteem as a Worker subscale. Furthermore, results revealed that only the Individual Self-Esteem as a Worker subscale changes significantly when people obtain employment. Finally, work satisfaction and particularly items related to satisfaction regarding the supervisor were significantly related to the Individual Self-Esteem as a Worker subscale. Avenues of research are discussed concerning the crucial role of the supervisor in improving the self-esteem as a worker and the work integration of people with severe mental disorders.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".