<p>Glasgow Sleep Effort Scale: Translation, Test, and Evaluation of Psychometric Properties of the Persian Version</p>
Bibliographic record
Abstract
PURPOSE: The purpose of the current study is to translate, test and evaluate the psychometric properties of the Glasgow Sleep Effort Scale (GSES) in Persian language. METHODS: Participants consisted of two samples: a clinical sample of 120 patients (58%) with insomnia disorder meeting DSM-5 criteria for insomnia and a non-clinical sample of 110 participants (42%) with normal sleep. Both samples completed the following measures: GSES, Pittsburg Sleep Quality Index, Insomnia Severity Index, Dysfunctional Beliefs and Attitudes about Sleep Scale-10, Pre Sleep Arousal Scale-cognitive subscale, Depression-Anxiety-Stress Scale-21 and sleep diary. RESULTS: Significant correlations were found between GSES and related measures in both groups. Principal component analysis indicated a single component accounted for 64.77% of total variance in the clinical group. Results of the fit estimates for the one-factor model were consistent with the previously specified fit criteria and adequately fitted the data in the non-clinical group. Statistical analyses showed that the GSES has acceptable internal consistency in terms of Cronbach's Alpha in the clinical (0.75) and non-clinical (0.77) samples. Test-retest reliability for a 4-week interval was significant (r = 0.70). The cut-off point, sensitivity, and specificity of the scale were 6, 85% and 94.5%, respectively. CONCLUSION: The Persian translated and validated version of the GSES obtained adequate values in psychometric properties in both clinical and non-clinical samples and it can be used for research and clinical purposes in Iran.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 0.002 |
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".