Alexithymia in athletic populations: Prevalence, and relationship with self-control and reinvestment
Bibliographic record
Abstract
Alexithymia is the inability to identify or describe feelings, with a tendency for externally oriented thinking; these facets have potential benefits for athletic performance. This study explored the prevalence of alexithymia among athletes, across different sports and athletic ability, and considered the relationship between alexithymia and trait self-control, and between alexithymia and reinvestment. Athletes ( N = 787) completed a 15-min online survey which comprised self-report questionnaires (e.g., demographic, Toronto Alexithymia Scale, Movement Specific Reinvestment Scale (MSRS), Decision Specific Reinvestment Scale (DSRS), and The Brief Self-Control Scale). The overall prevalence of high-alexithymia was notable in an athletic population; analyzes revealed that high-static-dynamic sports had higher alexithymia scores compared to low-static-dynamic sports. Athletes with higher alexithymia scores were related to lower trait self-control, in addition to higher MSRS and DSRS scores. The findings of the present study suggest that alexithymic athletes experience emotional dysregulation issues, are more likely to engage in risky behaviors, and engage in processes which are detrimental to their performance. This study represents an initial exploration, and future research should expand upon these findings to fully determine the performance outcomes of alexithymia in sport.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".