Evaluating Timeframe Expectancies in Physical Activity Social Cognition: Are Short- and Long-Term Motives Different?
Bibliographic record
Abstract
Promoting maintenance of regular physical activity (PA) is a public health priority; however, to the authors' knowledge, no researchers to date have examined whether the expectancies of proximal PA enactment are similar to the expectancies of longer maintenance. Thus, the authors' purpose in this study was to evaluate whether PA expectancies, measured with constructs of the theory of planned behavior (TPB), varied as a function of time frame (no time frame, next week, next month, next 6 months). Undergraduate students (N=409) completed randomly distributed self-report measures of the TPB; the authors then compared results across the 4 groups (formed on the basis of time frame). Analysis of variance tests showed that 13 of 37 constructs were significantly (p<.05) different, and post hoc follow-up tests identified that the proximal time frame (ie, next week) had the significantly lowest mean value. Chi-square tests of independent correlations, however, revealed few differences in TPB-intention correlations by time frame. The results suggest that social cognitive correlates of PA intention are robust to timeframe deviations but that time frame may affect the absolute values of some constructs. Overall, this is a positive finding because it suggests that PA promotion efforts focused on increasing expectancies do not have to be tailored to proximal or more distal maintenance applications.
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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 | 0.019 |
| 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 | 0.000 |
| 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".