What comes first? A test of two models to predict physical activity using self-efficacy and outcome expectancy
Bibliographic record
Abstract
According to Self-Efficacy Theory (SET), behaviours such as physical activity (PA) are influenced by self-efficacy via the mediating effect of outcome expectations. However, peculiar findings from certain studies have revealed that outcome expectancy may not directly influence PA, thus fuelling a debate regarding the extent to which this latter construct may impact PA. Such findings would also appear to call into question where self-efficacy falls in this sequence. Therefore, the purpose of this study was to examine these propositions by testing two theoretical models in predicting PA levels: the first with outcome expectancy as the mediator (SET), the second with self-efficacy in the mediating position. Participants were 225, predominantly female (65%) university students with a mean age of 20.7. The variables were assessed cross-sectionally through a self-report questionnaire available online. Results indicated a poor fit for the first model, as outcome expectancy was not a significant predictor of PA (s = .04, p = .56). Interestingly, there was a good fit for the second model as outcome expectancy influenced self-efficacy (s = .31, p < .001) and self-efficacy predicted PA (s= .38, p < .001). Although these findings failed to support the SET sequence, they suggest that perhaps the reverse may be true of behaviours that are not inherently pleasurable such as PA. Future interventions should outline clear outcome expectations prior to developing one's confidence for PA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 | 0.141 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".