Psychometric evaluation of the exercise-related cognitive errors questionnaire among Chinese emerging adults
Bibliographic record
Abstract
Background Cognitive errors involve negatively biased or distorted thinking patterns that can hinder effective decision-making. When such a phenomenon occurs in the exercise domain, this is referred to as exercise-related cognitive error. Such exercise-related cognitive errors are typically assessed via a questionnaire, but a validated instrument for the application in Chinese-speaking populations is lacking. Thus, this study aims to validate the Chinese version of the Exercise-related Cognitive Errors Questionnaire (E-CEQ-C) among Chinese emerging adults, a self-report measure to evaluate cognitive errors of context-relevant information related to exercise. Methods Following a forward-backward translation of the E-CEQ (N = 24 items), the E-CEQ-C and the Chinese version of the Cognitive Distortions Questionnaire (CD-Quest-C) for gathering evidence of criterion-related validity were administered among a sample of Chinese emerging adults (N = 376, 29.0% male) through an online survey. After a two-week interval, 105 out of 376 participants attended a re-test of the E-CEQ-C. Item analysis, confirmatory factor analysis (CFA), internal consistency, test–retest reliability, and concurrent validity were analyzed. Results The findings from the CFA support the 24-item informed six-factor structure among Chinese emerging adults (χ2 = 699.038, RMSEA = 0.073, CFI = 0.919, TLI = 0.904, and SRMR = 0.055). Cronbach’s α of the six dimensions of the E-CEQ-C were all above 0.7. The test–retest reliability coefficients of each subscale and total scale were acceptable, ranging from 0.60 to 0.81. In accordance with the literature, we also observed positive associations between the six dimensions of E-CEQ-C and the constructs of the CD-Quest-C, which provided concurrent validity evidence for the E-CEQ-C. Conclusion This study showed that E-CEQ-C is a psychometrically sound measure to assess exercise-related cognitive errors in Chinese-speaking populations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".