Comparison of the Impacts of PA Beliefs on PA Intention and Behavior Before and After COVID-19: Based on the Theory of Planned Behavior
Bibliographic record
Abstract
Background: The emergence of the COVID-19 has obstructed people from participating in various activities. University students were unable to take classes in person and to be involved in school clubs or voluntary activities. Eventually, it raised their sedentary behavior and physical inactivity during this critical period of habit formation. Purpose: The present study aimed to compare beliefs influencing physical activity (PA) intention and behavior before and after the COVID-19 outbreak based on the theory of planned behavior. Methods: Data were gathered three times from Korean undergraduate students. Participants’ beliefs about PA were elicited by think-aloud interview using an open-ended questionnaire. Their answers were analyzed by content analysis and used to make a closed-ended questionnaire for the second survey. Finally, data were collected one month after the second survey for a follow-up survey on PA participation. To understand the influence of salient PA beliefs on PA intention and behavior, data were analyzed using structural equation modeling. Results: There have been quite changes in salient beliefs of PA after emergence of COVID-19. Moreover, despite participation in PA was reduced by half (t = -5.70, p < 0.01), intention to PA has been significantly increased (t = 3.13, p < 0.01). The results also showed that the salient beliefs influencing PA intention and behavior after emergence of COVID-19 were completely different from those before COVID-19. Conclusions: It is estimated that the emergence of COVID-19 has brought changes in PA behavior by transforming various psychological mechanisms.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".