Emotional management training in residential mental health services
Bibliographic record
Abstract
A core element for the treatment of psychiatric patients in mental health services is the Psychosocial Rehabilitation. In this work we mainly refer to a training whose targets are fundamental components of the Emotional Intelligence (EI), which is, according to the original Salovey and Mayer's definition (1990), “a set of skills hypothesized to contribute to the accurate appraisal and expression of emotion in oneself and in others, the effective regulation of emotion in self and others, and the use of feelings to motivate, plan, and achieve in one's life”. The purpose of this study is to evaluate the efficacy of Emotional Management Training and to compare our emotional management assessment to standardized emotional intelligence assessment instruments. Twenty adult inpatients (from 18 to 55 years of age) were enrolled: ten subjects were assigned to a one year lasting emotional management training (clinical target group) and ten subjects were assigned to a clinical control group; furthermore twenty subjects were selected and assigned to a non-clinical control group. Outcome measures were: emotional management assessment, Schutte Emotional Intelligence Scale (SEIS) and Toronto Alexithymia Scale (TAS-20). Emotional management assessment outcomes confirm the efficacy of emotional management training. Preliminary results also confirm the effectiveness of the assessment compared to standardized emotional intelligence scales. Emotional management training improves psychiatric patient competence in terms of: emotions definition and acknowledgement, self-emotion identification, self-emotion sharing, management of stressing situation and intense emotions. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".