An experimental test of reframing counselling to attenuate biased exercise thoughts for individuals about to begin a diabetes prevention program
Bibliographic record
Abstract
BACKGROUND: Exercise-related cognitive errors (ECEs) represent a bias in information processing which distorts individuals' view of exercise. Such thinking can inhibit individuals' behaviour change efforts. Reframing is an evidence-based counselling strategy used to help individuals evaluate the evidence for and against their biased thoughts. The goal of reframing is to modify biased cognitions not to directly change an outcome behaviour for which behavioural strategies would be used. OBJECTIVE: To examine reframing as a pre-intervention strategy to attenuate ECE thinking as individuals begin a diet and exercise diabetes prevention program. METHODS: Prior to beginning a 3-week program, individuals diagnosed with prediabetes (N = 26, 18 female, Mage=58) were randomized to either 15-minutes of reframing counselling (REF) or attention control (AC). Those receiving REF were prompted to identify, challenge, and reframe their negative exercise thoughts. Changes in ECEs were measured at pre-test, post-test, and 4 weeks post-program using the exercise-related cognitive errors scale (range: 1-9). RESULTS: REF participants (n=14) experienced greater decreases in their ECE scores compared to AC (n=12) from baseline (REF =5.2, AC=5.0) to immediately post-program (REF=4.4, AC=5.1) and 4-weeks post-program (REF=3.2, AC=4.1). There was a significant main effect for time (p=.001, partial eta squared=.43). Those in REF experienced a greater ECE decrease across the study (Cohen's d =.71). CONCLUSION: A reduction in ECEs was observed immediately post-program for REF. The REF group also experienced greater reductions 4 weeks post-program. REF may be an effective means to help individuals reduce their biased exercise thoughts when making behaviour change efforts.Acknowledgments: Diabetes Canada Postdoctoral Fellowship; Michael Smith Foundation for Health Research Postdoctoral Fellowship
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".