Social norms and physical activity: A scoping review
Bibliographic record
Abstract
Social norms are unwritten rules about how to behave in a particular group or culture, and have been shown to impact behaviour change in many domains (Draper et al., 2015). However, there are equivocal findings regarding the influence of social norms in the context of physical activity (PA), which may be attributed to inconsistencies in the definition and measurement of social norms (Ball et al., 2010). The present scoping review analyzed the literature examining relationships between social norms and PA to specifically investigate how researchers have defined and measured social norms and how social norm-based interventions have been conducted. Articles were retrieved based on keyword searches of five electronic databases and manual searches of 14 relevant journals. A total of 129 articles meeting the inclusion criteria were reviewed. The majority of articles (n = 119) measured subjective norms within the theory of planned behaviour (TPB) and revealed inconclusive results regarding the ability of subjective norms to predict PA intentions and behaviours. The TPB specific results also illustrated several factors that should be considered when examining the role of subjective norms in the context of PA behaviours, which included measurement issues, potential covariates of subjective norms, and moderators of the subjective norm-intention relationship. Among studies not grounded within the TPB (n = 10), three studies measured, and seven manipulated, other forms of social norms (e.g., descriptive norms) and found promising results in facilitating PA. Researchers may use the current findings to target appropriate populations for interventions, refine measurement, and incorporate relevant types of norms.
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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 | 0.018 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.028 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".