Passion transfer in former competitive athletes: The mediating role of the social environment and personal values
Bibliographic record
Abstract
Athletes often report being passionate about their sport, but little is known about how their passion evolves after they retire from competitive sports. The Dualistic Model of Passion postulates that harmonious (HP) and obsessive passions (OP) can transfer to another related activity. This cross-sectional study examined two processes (social environment and personal values) through which an old passion for competitive sports transfers to a new related activity and their influence on the type of passion for this new activity. We performed structural equation modeling with former competitive athletes now engaged in coaching (n = 120) or playing recreationally (n = 318). Results revealed that the old HP was positively associated with selecting an autonomy-supportive environment and prosocial values, which, in turn, were positively related to HP for the new activity. Conversely, the old OP was positively related to a controlling environment and proself values, which were positively related to OP for the new activity. • Social environment and personal values affect the transfer of passion. • Harmonious passion links to autonomy-supportive environments and prosocial values. • Obsessive passion links to controlling environments and proself values. • Person-environment fit facilitates passion quality transfer in related activities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".