Crime and Physical Activity: Development of a Conceptual Framework and Measures
Bibliographic record
Abstract
BACKGROUND: A common hypothesis is that crime is a major barrier to physical activity, but research does not consistently support this assumption. This article advances research on crime-related safety and physical activity by developing a multilevel conceptual framework and reliable measures applicable across age groups. METHODS: Criminologists and physical activity researchers collaborated to develop a conceptual framework. Survey development involved qualitative data collection and resulted in 155 items and 26 scales. Intraclass correlation coefficients (ICCs) were computed to assess test-retest reliability in a subsample of participants (N = 176). Analyses were conducted separately by age groups. RESULTS: Test-retest reliability for most scales (63 of 104 ICCs across 4 age groups) was "excellent" or "good" (ICC ≥ .60) and only 18 ICCs were "poor" (ICC < .40). Reliability varied by age group. Adolescents (aged 12-17 y) had ICCs above the .40 threshold for 21 of 26 scales (81%). Young adults (aged 18-39 y) and middle-aged adults (aged 40-65 y) had ICCs above .40 for 24 (92%) and 23 (88%) scales, respectively. Older adults (aged 66 y and older) had ICCs above .40 for 18 of 26 scales (69%). CONCLUSIONS: The conceptual framework and reliable measures can be used to clarify the inconclusive relationships between crime-related safety and physical activity.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".