The moderating influence of outcome value: Its role in the outcome expectancy – exercise behaviour relationship
Bibliographic record
Abstract
Expectancy-value theories consider anticipated outcomes of engaging in exercise and associated value of these outcomes as key predictors of exercise behaviour. Research to support the link between outcome expectancies (OE), outcome value (OV), and exercise behaviour, however, is weak (Williams, Anderson, & Winnett, 2005). It has been suggested that the relationship between OE and exercise is important but masked by an underlying moderation effect by OV. To explore this potential moderated relationship, measures of physical, social, and psychological OE and OV, as well as exercise behaviour, were assessed in 445 young adults (Mage = 24.5). Each OV subclassification (e.g., physical, social, and psychological) was tested as the moderator of the relationship between its corresponding OE and exercise behaviour. Out of all analyses, only social OV moderated the relationship between social OE and exercise behaviour. The overall regression model was significant (Adj R2 = .02, p = .01), with social OE significantly contributing to the prediction of exercise behaviour (std. beta = .18, p < .01). The interaction term was significant (b = .76, p = .03) thus warranting further examination. The positive association between exercise and social OE increased in strength when social outcomes were of greater value. These findings support the notion that OE only influence exercise behaviour if they are valued, and allude to the importance young adults place on the social outcomes of exercise.
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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 | 0.013 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".