Do exercise identity and social cognitions predict who meets Canadian exercise guidelines
Bibliographic record
Abstract
Individuals with stronger exercise identity (EXID) report more exercise and greater strength of self-regulatory cognitions like self-efficacy than low-identity counterparts (Strachan et al., 2009). While EXID is strongly related to moderate-to-vigorous exercise (mod+) volume, it is unknown whether EXID and self-regulatory cognitions discriminate individuals who meet/do not meet Canadian guidelines (i.e., 150 minutes/week mod+ exercise; CSEP, 2011). We examined 233 university students active at different levels. Their EXID, self-regulatory efficacy, perseverance, strategies to stay active and strength of use were assessed. A logistic regression was performed. The full model was significant, ?2 (5, N = 233) = 48.92, p < .001, and discriminated participants who met/did not meet guidelines. The model explained 25.7% (Nagelkerke R square) of the variance and correctly classified 73.4% of cases. All predictors except the number of strategies contributed significantly with EXID being the strongest. Results support complementary use of identity and social cognitive theory and suggest that individuals with stronger levels of EXID and self-regulatory cognitions are at a motivational advantage for being active at the recommended guideline level.Acknowledgments: Support from the Social Sciences and Humanities Research Council (SSHRC) is gratefully acknowledged.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".