The validity and reliability of the Persian version of the Athlete Sleep Screening Questionnaire
Bibliographic record
Abstract
Background: Sleep as a biological phenomenon is effective in the performance and recovery of athletes. Questionnaires can be used as a cost-effective initial assessment tool for sleep. The Athlete Sleep Screening Questionnaire (ASSQ) demonstrated a clinically valid instrument for screening relevant sleep issues in athletic populations. Due to the lack of validated tools for adequate screening for sleep difficulties in the Iranian athlete population, the present study was conducted to evaluate the validity and reliability of the Persian version of the ASSQ. Materials and Methods: . Content validity was assessed by a panel of experts. Exploratory and confirmatory factor analysis was performed for two 5-item sleep difficulty scores (SDS) and a 4-item chronotype score. Internal consistency based on Cronbach's alpha and McDonald's omega and stability reliability were used to evaluate reliability. Results: The ASSQ achieved conceptual and semantic equivalence with the original scale. The item-level content validity index (I-CVI) of each item ranged from 0.87 to 1, and the averaging scale-level CVI/average was 0.95. In factor analysis, one factor for SDS and one factor for chronotype score were identified and confirmed. The internal consistency of the SDS scale (α =0.77, Ω =0.83) and chronotype (α =0.74, Ω =0.77) was acceptable. Stability reliability was confirmed for SDS scale (intra-class correlation [ICC] =0.87) and for chronotype (ICC = 0.83). Conclusion: Persian ASSQ has acceptable psychometric measurement properties as a screening tool to assess sleep in Iranian athletes.
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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 | 0.005 | 0.012 |
| 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 | 0.000 |
| 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".