Passion and engagement among athletes and coaches: A look using a quadripartite approach
Bibliographic record
Abstract
The dualistic model of passion (Vallerand, 2015) distinguishes between harmonious and obsessive dimensions of passion. In sport, harmonious passion tends to be positively associated with adaptive sport outcomes, whereas obsessive passion tends to be positively associated with maladaptive sport outcomes. However, in the educational literature, both harmonious and obsessive passion have been linked with greater academic engagement. In this research, we tested if both passion dimensions were associated with greater levels of engagement in sport using the quadripartite approach (Schellenberg et al., 2019). We collected data from samples of athletes (N = 403) and coaches (N = 208) who completed online surveys assessing harmonious passion, obsessive passion, and different components of sport engagement (confidence, vigor, dedication, enthusiasm; Lonsdale et al., 2007). In both samples, results showed that all dimensions of engagement were positively associated with harmonious passion. In contrast, obsessive passion was negatively associated with vigor and enthusiasm (athlete sample), but positively associated with dedication (coach sample). Using a quadripartite approach, we found that the highest levels of dedication among coaches were for those with high harmonious passion and high obsessive passion, a result that supports findings in educational contexts. However, for all other engagement dimensions in both samples, the highest levels of engagement were associated with high harmonious passion, particularly when combined with low obsessive passion. These results mean that sport passion does not always translate into sport engagement; to achieve the highest levels of engagement, passion needs to involve high levels of harmonious passion.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".