Predictors and outcomes of core and peripheral sport motivation profiles: A person-centered study
Bibliographic record
Abstract
While previous studies highlighted the importance of the different motivations for doing sports as proposed by self-determination theory, less emphasis has been put on the simultaneous presence of multiple motivations within the same individual. Therefore, the present study aimed to investigate the complex interaction of sport motivations and to identify core (common) and peripheral (uncommon) profiles of people engaged in sports based on a combination of motivations. To achieve this goal, latent profile analysis, a person-centered approach, was performed on responses from 506 participants engaged in sports. For better understanding the extracted profiles, basic psychological need fulfillment was included as profile predictor, while subjective vitality and various engagement-related indicators as outcomes. Four core and peripheral profiles were identified: Moderately Motivated, Highly Motivated, Amotivated, and Poorly Motivated. Contrary to theory, introjected regulation clustered more closely with self-determined motivations. Profile membership was significantly predicted by global need fulfillment, autonomy satisfaction as well as, to a smaller extent, autonomy, relatedness, and competence frustration. The four profiles differed along vitality and some, but not all, engagement-related outcomes.
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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 | 0.003 |
| 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) | 0.001 | 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".