Using short vignettes to disentangle perceived capability from motivation: a test using walking and resistance training behaviors
Bibliographic record
Abstract
Self-efficacy is arguably the strongest correlate of physical activity, yet some researchers suggest this is because the construct confounds ability with motivation. We examine a more circumscribed construct, called perceived capability (PC), meant to measure ability but not motivation and propose that the construct will not be related to unskilled physical activities but may be linked to skilled behaviors. The purpose of this paper was to examine whether a PC construct can be stripped of motivation using a vignette approach in both walking and resistance training behaviors. Participants were a random sample of 248 university students, who were then randomly assigned to either answer resistance training or walking behavior questions. Both groups completed a PC measure and reasons for their answer before and after reading a vignette that clarified the phrasing of capability to a literal use of the term. PC was significantly (p < .01) higher post- compared to pre-vignette and the differences were greater (p < .01) for walking than for resistance training. PC had significantly (p < .01) smaller correlations with intention and self-reported behavior post-disambiguation, which resulted in a null relationship with walking but a small correlation with resistance training behavior. When PC was combined with intention to predict behavior, however, there was no significant (p > .05) difference in the amount of variance explained pre- to post-vignette. Thought listing showed that participants did not report capability barriers to walking and over half of the sample construed capability as motivation/other priorities pre-vignette. The findings support use of a vignette approach for researchers who wish to disentangle the assessment of PC from motivation while creating no overall loss in explained variance of physical activity.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".