Self-compassion, depressive symptoms, and well-being: A cross-sectional exploration across athlete status and gender
Bibliographic record
Abstract
Self-compassion is associated with positive mental health outcomes and may buffer against negative self-evaluations and emotional difficulties. Nevertheless, studies among athletes often explore self-compassion in specific groups in isolation (e.g., women athletes) (Röthlin et al., 2019). The aims of this study were to 1) explore whether the relationship between gender and composite scores and specific dimensions of self-compassion (e.g., self-judgement) was moderated by athlete status; and 2) to explore the relationship between different dimensions of self-compassion and self-reported depressive symptoms and well-being among team sport athletes ( n = 84, M age = 22.9 ± 5.0; 57.1 %men) and non-athletes ( n = 189, M age = 35.5 ± 5.9; 32.8 %men). For our first aim, the relationship between gender and self-compassion (including specific dimensions) was not moderated by athlete status. However, regardless of gender, athletes reported significantly higher total self-compassion scores and significantly lower scores on specific dimensions of self-compassion, isolation, and over-identification, than non-athletes. For our second aim, self-judgement was positively associated with depressive symptoms in both athletes and non-athletes. Self-judgment was, however, negatively associated with well-being only among athletes, and isolation was negatively correlated with well-being only among non-athletes. Our results suggest that reducing self-judgement may be particularly important for promoting athletes’ mental health
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".