Validation of the exercise-related cognitive errors questionnaire short form
Bibliographic record
Abstract
Exercise-related cognitive error represent the extent to which individuals view their exercise engagement through a negative and biased lens. Three datasets were examined to develop a short form of the original 16-item exercise-related cognitive errors questionnaire (E-CEQ) and evaluate evidence of validity. Exploratory factor analysis on datasets 1 ( N = 394), 2 ( N = 177), and 3 ( N = 1027) suggested that a seven-item, one-factor model fit the data. Findings suggested that the ECEQ short form had a unidimensional factor structure that did not vary based on age or gender. As evidence of criterion-related validity, similar magnitude correlations were observed for the E-CEQ short-form (ECEQ-SF) and the original E-CEQ with key exercise variables in datasets 1 and 2 (| rs | ranged from .20 to .76). The ECEQ-SF captures the extent to which individuals view their perceived exercise barriers through a cognitively errored lens.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".