Understanding Physical Activity Behavior in African American and Caucasian College Students: An Application of the Theory of Planned Behavior
Bibliographic record
Abstract
UNLABELLED: Only 30% of college students meet the recommended amount of physical activity (PA) for health benefits, and this number is lower for African American students. Moreover, the correlates of PA may vary by ethnicity. OBJECTIVE: In the present study, the authors tested the utility of the theory of planned behavior for explaining PA intentions and behavior in Caucasian and African American students. PARTICIPANTS AND METHODS: Participants were 238 African American (M age = 20.05 years, SD = 2.28) and 197 Caucasian (M age = 19.50 years, SD = 2.28) students who completed a baseline theory of planned behavior questionnaire and a follow-up PA measure 1 week later. RESULTS: Hierarchical regression analyses showed that affective (beta = .23) and instrumental (beta = .28) attitudes and perceived behavioral control (beta = .59) were significantly predictive of intention for the Caucasian students, whereas affective attitude (beta = .18) and perceived behavioral control (beta = .56) were significant for African American students. Furthermore, intention (beta = .33) was the lone significant predictor of PA for Caucasian students, whereas perceived behavioral control (beta = .23) was the significant predictor of PA for African American students. CONCLUSIONS: These data suggest that practitioners may need to consider ethnicity when developing PA interventions for college students based on the theory of planned behavior.
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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 | 0.008 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".