Applying self-compassion to address the negative factors related to early retirement among South Korean student athletes
Bibliographic record
Abstract
Self-compassion is relatively a new psychological construct that could help elite athletes to positively deal/cope with negative experiences. To evaluate the construct of self-compassion as a means to address negative factors related to South Korean student athletes' early retirement, the current study narratively reviewed self-compassion, which has been shown to be an effective means to positively influence individuals' psychological well-being. This study critically analyzed and narratively reviewed self-compassion in order to apply the construct to help Korean student athletes cope with the negative factors of early retirement. In addition, the study conducted an unsystematic narrative review (see Green, Johnson, & Adam, 2006) to summarize the contents of previously published data. Self-compassion consists of three elements that include self-kindness, common humanity, and mindfulness. Self-compassion involves being open and touched by one's own suffering and offering non-judgmental understanding, patient, and kindness, recognizing that all humans are imperfect, can fail, and make mistakes. Previous studies have commonly utilized the construct as a tool for addressing elite athletes' injuries, setback, emotional pains, and eudaimonic well-being. However, self-compassion has never been discussed by Korean researchers (in the elite sports context). The Major reasons for Korean student athletes' early retirement are injuries, stresses of training, competition, future career aspiration, pressures to succeed, or/and loss of enjoyment(interest). Self-compassion could be one of the means by elite athletes that positively deal or cope with the negative factors of their early retirement. The current study would introduce the new psychological concept to Korean research and contribute to promoting research on self-compassion related to Korean student and elite athletes.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".