Outcome expectations in exercise: Distinguishing between likelihood and desirability
Bibliographic record
Abstract
Background: Outcome expectations from Bandura's (1986) social-cognitive theory have been inconsistently conceptualized in the literature. The perceived likelihood of an outcome happening is one aspect, but the extent to which one values that outcome is also important. The relationship between the likelihood and desirability of an outcome to each other and to behavioural intentions and exercise behaviour is not well understood. Objectives: The purpose of this study was to examine the respective factor structures of the likelihood and desirability components of common exercise outcome expectations. This was accomplished by using the Exercise Outcome Expectations Questionnaire (EOEQ), which separately assesses the likelihood and desirability for commonly reported outcomes of exercise. A secondary aspect of the research was a test of the psychometric quality of the questionnaire. Methods: EOEQ data from 459 non-exercisers (69% female, M age = 48.26 years, SD = 8.52) were analyzed using confirmatory factor analysis. Results: Analysis supported a six-factor model for outcome likelihood, including Physical Health, Mental Health/Stress, Appearance, Fitness, Vitality, and Enjoyment. A similar model was supported for outcome desirability, but with Enjoyment replaced by Negative Outcomes. Measurement invariance by gender was established for the desirability factors, but not for the likelihood factors. Conclusions: This study suggests that the likelihood and desirability aspects of outcome expectations are probably independent of each other. It also provides preliminary evidence for the utility of the EOEQ in measuring the perceived likelihood and desirability of exercise outcomes. Future research examining the contributions of these factors to exercise behaviour is needed.Acknowledgments: This research was supported by the Canadian Institutes of Health Research.
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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 | 0.010 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".