Motivational and Personality Predictors of Body Esteem in High- and Low-Frequency Exercisers
Bibliographic record
Abstract
Active living is imperative to maintaining good health, and becoming involved in regular exercise at a young age is fundamental. The purpose of this study was to examine motivation for exercise among university students in relation to metamotivational dominance and body esteem. Participants in this study were 106 undergraduate students who were recruited from their psychology departmental participant pool and from the campus exercise facility at a medium sized Canadian university. Participants completed an inventory that included the Motivational Style Profile, Big Five Inventory-10, Behavioral Regulation in Exercise Questionnaire, and the Body Weight and Image Self-Esteem Evaluation Questionnaire to assess personality, exercise motivation, and body esteem. High-frequency exercisers were found to be more paratelic dominant than low-frequency exercisers, and scored significantly higher on intrinsic, identified, and introjected regulation, indicating that they exercised for enjoyment, valued exercise outcomes, and wanted to avoid negative emotions associated with not exercising. Among high frequency exercisers, positive body esteem was associated with high intrinsic and low extrinsic motivation for exercise, paratelic dominance, negativism dominance, and low neuroticism. For low-frequency exercisers, significant correlates of positive body esteem were autic mastery dominance, low BMI, low neuroticism, and lower levels of extrinsic and introjected motivation. Findings are discussed in terms of healthy and unhealthy motivations for exercise, and recommendations are made for tailoring health promotion strategies to metamotivational dominance.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".