Examining physical activity among US college students following COVID-19 outbreaks and lockdowns : the role of race/ethnicity and acculturation
Bibliographic record
Abstract
Objective: This study examined physical activity among US college students in the context following COVID-19 outbreaks and lockdowns, with a focus on racial/ethnic and cultural determinants. Sample: This study used cross-sectional data of the COVID-19 University Research on Education and Sustainability (CURES) project. Participants were college students from 7 public universities in the US (N = 1210; 75% female; 33.5% White, 9.1% Black, 47.6% Hispanic, and 7.1% Asian; Mage = 21.06; 85.6% born in the US, 51.6% had mother born in the US, and 49% had father born in the US). Method: The International Physical Activity Questionnaire - short form (IPAQ-SF) was used to assess physical activity and the Vancouver Index of Acculturation (VIA) – the American version was used to assess levels of heritage and US acculturation. Results: White students reported statistically significant higher physical activity than Hispanic (p < .05, Cohen’s d = .19) and Asian (p < .05, Cohen’s d = .36). In sedentary level, White students reported statistically significant lower levels than Asian (p < .05, Cohen’s d = -.45). Asian reported lowest physical activity, highest sedentary level, and highest prevalence of not meeting physical activity recommendation. Small negative correlation was found between heritage cultural level and total weekly physical activity among Hispanic students (r = - .09, p < .05). Conclusion: Race/ethnicity and cultural perception and practices could play a role in determine physical activity and sedentary behaviors among college students. The study calls for more qualitative research, and racial/ethnic and cultural-specific interventions to improve physical activity among Hispanic/Latin and Asian student populations.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".