The Relationship of Self-efficacy and Explicit and Implicit Associations on the Intention–Behavior Gap
Bibliographic record
Abstract
BACKGROUND: Recent physical activity research is limited by intention-behavior discordance and is beginning to recognize the importance of automatic processes in exercise. The purpose of the current study was to examine the role of multidimensional exercise self-efficacy (SE), explicit-implicit evaluative discrepancies (EIEDs) for health, and appearance on the intention-behavior gap in exercise. METHODS: A total of 141 middle-aged inactive participants (mean age = 46.12 [8.17] y) completed measures of intentions, SE, and explicit and implicit evaluations of exercise outcomes. The participants were classified as inclined actors (n = 107) if they successfully started the exercise program and inclined abstainers (n = 35) if they were not successful. RESULTS: The inclined actors and abstainers did not differ on intentions to exercise; however, the inclined actors had higher coping SE and lower EIEDs for health. In addition, the coping SE (Exp [β] = 1.03) and EIEDs for health (Exp [β] = -0.405) were significant predictors of being an inclined actor. CONCLUSIONS: The interaction between explicit and implicit processes in regard to health motives for exercise appears to influence the successful enactment of exercise from positive intentions. As most physical activity promotion strategies focus on health as a reason to be active, the role of implicit and explicit evaluations on behavioral decisions to exercise may inform future interventions.
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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 | 0.004 | 0.021 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".