Preliminary data concerning the reliability and psychometric properties of the Greek translation of the 20-item Subjective Well-Being Under Neuroleptic Treatment Scale (SWN-20)
Bibliographic record
Abstract
BACKGROUND: The 20-item Subjective Well-Being Under Neuroleptic Treatment Scale (SWN-20) is a self-report scale developed in order to assess the well-being of patients receiving antipsychotic medication independent of the improvement in their psychotic symptoms. The current study reports on the reliability and the psychometric properties of the Greek translation of the SWN-20. METHODS: A total of 100 inpatients or outpatients with schizophrenia (79 males and 21 females, aged 42.6 +/- 11.35 years old) from 3 different facilities were assessed with the Positive and Negative Symptoms Scale (PANSS), the Calgary Depression Scale and the Simpson-Angus Scale, and completed the SWN-20. The statistical analysis included the calculation of Pearson product moment correlation coefficient, the Cronbach alpha and factor analysis with Varimax normalised rotation. RESULTS: The SWN-20 had an alpha value equal to 0.79 and all the items were equal. The factor analysis revealed the presence of seven factors explaining 66% of total variance. The correlation matrix revealed a moderate relationship of the SWN-20 and its factors with the PANSS-Negative (PANSS-N), PANSS-General Psychopathology (PANSS-G), the Simpson-Angus and the Calgary scales, and no relationship to age, education and income class. DISCUSSION: The Greek translation of the SWN-20 is reliable, with psychometric properties close to the original scale.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".