Cognitive reframing: Implementing a novel strategy to challenge exercise-related cognitive errors
Bibliographic record
Abstract
Exercise-related cognitive errors (ECEs) represent a biased view of information that distorts individuals' view of exercise. Individuals with greater ECEs express more struggle in deciding to exercise, lower confidence to manage exercise, and have lower and more erratic exercise engagement. Cognitive reframing is an evidence-based counselling strategy whereby individuals are taught to identify, challenge, and reframe their unhelpful and negative thoughts. The present study assessed the influence of a cognitive reframing strategy tailored for exercise in modifying negative exercise thoughts of individuals making ECEs. Adults struggling to regularly exercise (N=12) received a 20-minute cognitive reframing session. Pre- and post-reframing cognitions were assessed (i.e., ECEs, decisional struggle, self-regulatory efficacy to manage ECEs [SRE], exercise intention). Overall, participants reduced their ECE level (Mean change=.57, Cohen's d=.27) and decisional struggle (Mean change=2.94, Cohen's d=2.1), and increased their intentions (Mean change=1.43, Cohen's d=.47) and SRE (Mean change=22.6, Cohen's d=.74). The reframing process will be illustrated via a participant case. Pam initially viewed exercise through the Catastrophizing ECE, believing she was always being judged when she exercised. She used reframing to change that thought to, very few people might actually try to judge me. She was also overwhelmed by the thought that the only way to fitness was through intense exercise; an All-or-Nothing ECE. She reframed this view to, maybe I can be successful by starting slow. Cognitive reframing may hold potential as a cognitive strategy to help individuals who view exercise through the biased thinking of ECEs.Acknowledgments: Diabetes Canada Postdoctoral Fellowship; MSFHR Postdoctoral Fellowship; 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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 | 0.000 |
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".