Bibliographic record
Abstract
Objective: Cognitive errors (CEs) reflect individuals' biased evaluations of context-relevant information. In the exercise domain, a valid form of exercise CE assessment is needed. The Exercise-related Cognitive Errors Questionnaire (E-CEQ) was developed to determine to what extent adults make cognitive errors regarding exercise decisions. The purpose of this study was to develop and provide initial validity evidence for the E-CEQ. Design: The current study used an online self-report survey. Method: First, 24 initial vignettes representing 6 types of CEs were created. Evidence of content validity is discussed. Second, data from 364 adults was gathered to examine the E-CEQ's factor structure. Third, aspects of criterion-related validity were examined (e.g., the E-CEQ's utility in predicting physical activity and adherence cognitions). Results: Content validity was demonstrated. A 9-item, 1-factor (a = .86) model was retained as the final E-CEQ factor structure and had excellent psychometric properties (?2=34.61, df=27, p>.05; RMSEA=.028; CFI=.990; TLI=.987). Regarding predictive utility, individuals expressing higher levels of CEs exercised less and reported problematic cognitions (e.g., more struggle with exercise decisions, lower self-regulatory efficacy). Conclusions: These results link CEs to weaker social cognitions and inconsistent adherence perceptions, a novel form of bias related to limited exercise engagement. The steps taken to examine different forms of validity helped provide a platform from which to continue (a) to study biases linked to cognitive errors and (b) the E-CEQ validation process through ongoing investigation.Acknowledgments: Social Sciences and Humanities Research Council of Canada (SSHRC) doctoral fellowship and from Canada Research Chair training funds.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".