Effect of Manipulating Descriptive Norms and Positive Outcome Expectations on Physical Activity of University Students During Exams
Bibliographic record
Abstract
This experimental study examined the interaction between messages conveying different levels of descriptive norms and positive outcome expectations on university students' engagement in moderate and vigorous physical activity over an exam period. Using a pre-post design, university students entering a final examination period (N = 74) were randomly assigned to one of four message conditions, receiving a message motivating them to exercise over the exam period. Messages included both a descriptive norm (how many others reported being active during a previous exam period; high vs. low) and a positive outcome expectation (those who exercise during exams report better grades; high vs. low). The results from an analysis of covariance (ANCOVA), controlling for baseline levels of daily physical activity, revealed a significant interaction. Post hoc analyses indicated that when the descriptive norm was high, those who received a high positive outcome expectation reported being more active during the exam period compared to those receiving the low positive outcome expectation. Results provide preliminary support for the idea that activity during an exam period can be positively influenced if individuals are presented with normative messages that (a) many others are being active during the exams and (b) many of those being active also are benefiting academically.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".