An Examination of Dweck’s Psychological Needs Model in Relation to Exercise-Related Well-Being
Bibliographic record
Abstract
This two-part study examined Dweck's psychological needs model in relation to exercise-related well-being and particularly focused on the basic need for optimal predictability and compound needs for identity and meaning. In Part 1 (N = 559), using exploratory factor analysis, scores derived from items assessing optimal predictability (prediction of affect and instrumental utility in exercise) were empirically distinct from scores derived from items assessing competence, relatedness, and autonomy. In Part 2, participants from Part 1 (N = 403) completed measures of exercise-related well-being 4 weeks after baseline assessment. Prediction of affect was the most consistent predictor of subsequent exercise-related well-being. An implication of these findings is that optimal predictability (primarily prediction of affect) represents a unique experience that may be necessary for thriving in the context of exercise. Prediction of affect should be targeted in experimental designs to further understand its relationship with exercise-related well-being.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".