Examining the interaction between descriptive norms and positive outcome expectations on students' exercise behaviour over a final exam period
Bibliographic record
Abstract
Focus theory of normative conduct (Cialdini et al.,1990) postulates that individuals are more likely to perform a behaviour if they perceive that a majority of others engage in that behaviour (descriptive norm; DN). However, norms only influence behaviour if the information is salient to the individual. While norms have been examined in exercise, little attention has been paid to salience. One potential method to enhance the salience of normative messages involves outlining the benefits of engaging in exercise (i.e., positive outcome expectations; Bandura, 1986). The purpose of this study was to examine the interaction between DNs and positive outcome expectations (OE) on exercise behaviour during a final exam period. Regularly active undergraduate students (N=74) were randomly assigned to receive one of four messages, which included both a DN (how many students reported being active; high=63% vs low=13%) and a positive OE (those who exercise during exams experience enhanced academic performance; high=90% vs low=10%). Activity during the exams was self-reported retrospectively (MAQ, Kriska et al., 1990). Results from a 2X2 ANCOVA, controlling for initial physical activity levels, revealed a significant interaction, F(1,64)=4.17, p=.04. Post-hoc analyses indicated that when the DN was high, those who received a high positive OE reported greater exercise compared to those who received a low positive OE (p=.01, estimated Cohen's d=0.85). In line with focus theory, exercise during the exam period was highest for those who received a message that many others had been active during previous exams, and the norm information had been made salient (high positive OE). Acknowledgments: Social Sciences and Humanities Research Council of Canada
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".