Walking versus riding: Increasing stair-use activity at the office through social norms
Bibliographic record
Abstract
Sedentary behaviour has been identified as having many negative health outcomes; however, simple activities such as taking the stairs can have a positive effect on health by breaking up sedentary time (Marshall & Ramirez, 2011). It might be especially important to encourage this activity in individuals with sedentary office jobs (Chau et al., 2010). One way to do this might be through the use of social norms, especially descriptive norms (perceptions of others’ behaviour), which have been shown to be an effective tool for changing behaviour in this population (Priebe & Spink, 2012). This study examined the influence of a descriptive norm message on stair use in an office setting. Given the importance of a psychologically meaningful group to norm influence (Hogg, 2003), group identity also was examined. After completing online measures assessing initial stair behaviour, office workers (N=97) received a fictional normative message via email about their co-workers behaviour relative to their own (i.e., your co-workers used the stairs instead of the elevator more than you did). Repeated measures ANOVA results revealed a significant increase in stair use from pre- to post-message (p’s < .05). However, this was qualified by a significant time/identity interaction (p < .10) where differences only emerged for those who highly identified with the norm reference group. The preliminary findings suggest that descriptive norm messages might be an effective tool for increasing stair use in workplace settings and, consistent with theory, highlight the importance of considering identifying with the norm reference group.Acknowledgments: 1st author supported by a Social Sciences and Humanities Research Council of Canada Vanier Graduate Scholarship.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".