Perceived variety, psychological needs satisfaction and exercise-related well-being
Bibliographic record
Abstract
OBJECTIVE: Perceived variety represents a psychosocial experience that gives rise to, and supports the maintenance of, an individual's well-being. In this study, we developed an instrument to measure perceived variety in exercise (PVE), and examined whether ratings of PVE predict unique variance in indices of exercise-related well-being in addition to that explained by satisfaction of the three basic psychological needs (for competence, relatedness and autonomy) embedded within self-determination theory (SDT). We also examined the extent to which variance in perceived variety is empirically distinct from (or subsumed by) competence, relatedness and autonomy in the context of exercise. METHODS: A convenience sample of community adults (N = 507) completed online surveys twice over a six-week period (n = 367). RESULTS: PVE was found to prospectively predict unique variance in indices of exercise-related well-being, in addition to that explained by perceived competence, relatedness and autonomy. Using exploratory and confirmatory factor analytic procedures, perceived variety was found to be empirically distinct from perceived competence, relatedness and autonomy. CONCLUSION: Results from this work suggest that perceived variety holds potential for theoretical and applied advancements in understanding and predicting well-being in exercise settings.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".