Examining the relationship between descriptive norms and self-regulatory efficacy in an activity setting
Bibliographic record
Abstract
While there is evidence that descriptive norms (others' behaviour) affect individual physical activity behaviour (PA; Priebe et al., 2009), little is known about how this effect occurs (Rimal, 2008). One possibility is that normative messages about others engaging in PA capture verbal persuasion and vicarious experience, two sources of efficacy identified by Bandura (1977). The purpose of this study was to examine whether a relationship existed between descriptive norms for PA and self-regulatory efficacy (SRE). Using an experimental design, university students were assigned to either a descriptive norm (n=51) or control (n=58) condition. Those in the norm condition received four email messages encouraging them to be active because other students were doing it while those in the control received four messages encouraging activity. Based on self-efficacy theory, it was predicted that SRE would be higher in the norm condition. While post-intervention SRE was higher in the norm versus the control condition, results from a t-test revealed that the difference was not significant (p > .05). Also, PA did not differ between conditions, a result consistent with the SRE findings. Further examination of SRE appears warranted for two reasons. First, a trend existed for higher SRE in the norm condition. Second, there exists the possibility that identity with the norm reference group used in the messages (i.e., students) may have been too low for SRE to be impacted (Rimal et al., 2005).Acknowledgments: Supported by a SSHRC Vanier Graduate Scholarship (1st author).
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".