Relationship between passion and psychological well-being of taekwondo athletes: testing the mediating effects of social behavior
Bibliographic record
Abstract
BACKGROUND: The study uses PROCESS Macro statistical model and examines the causal relationship of passion, social behavior, and psychological well-being of taekwondo athletes. METHODS: Passion, social behavior, and psychological well-being were measured among 261 registered athletes at Korea Taekwondo Association in 2021 in South Korea. The collected data was analyzed using SPSS 24.0 (SPSS Inc., Chicago, IL, USA), PROCESS Macro (Calgary, AB, Canada), Amos 24.0 (IBM Corp, Armonk, NY, USA). RESULTS: Harmonious passion had a significant indirect effect through prosocial behavior on the feelings of self-realization, confidence, and flow and through antisocial behavior on the feeling of flow. Obsessive passion had a significant indirect effect through prosocial behavior on the feelings of self-realization, confidence, and flow and through antisocial behavior on feeling of flow, hedonic enjoyment. CONCLUSIONS: The results of the study suggest that the level of perceiving psychological well-being differs based on the level of taekwondo athletes' dualistic passion. social behavior, the study is expected to serve as a model study that enables applying psychological well-being.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| 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".