Relationships between Exercise Regulations and Motivational Consequences in University Students
Bibliographic record
Abstract
The aim of this study was to examine propositions stemming from Self-Determination Theory (SDT) that contend motivational consequences vary as a function of different regulations in the exercise domain. Participants (N = 276; n = 98 men; n = 178 women) completed inventories assessing exercise regulations, current exercise behavior, and behavioral intentions to continue exercising for the next 4 months and the effort and importance associated with exercise participation. Bivariate analyses indicated autonomous exercise regulations (identified and intrinsic) were the strongest correlates of each motivational consequence across both sexes, and introjected regulation was positively associated with exercise consequences in women. Simultaneous multiple regression analyses indicated that exercise regulations accounted for a sizeable portion of the variance across each motivational consequence in both sexes (R2adj values ranged from .20 to .53). Both regression and structure coefficients revealed that introjected regulation was a stronger motivational force in women than men, and identified regulation was the most important predictor of all three motivational consequences in both sexes. Collectively, these findings suggest that exercise regulations differentially predict motivational consequences across sexes, and future research using this theoretical framework for examining motivational issues pertinent to the exercise domain appears warranted.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".