Is Professional Soccer a Risk for Their “Lives Afterwards”? A Social-Sciences-Based Examination of Retired Professional Soccer Players from a Long-Term Perspective
Bibliographic record
Abstract
Most professional soccer players’ careers end before their forties. Consequently, many of them face a relatively early retirement from their profession, thus facing multifaceted changes and potential issues of adjustments in different areas of their lives. Public discussion and therein expressed concerns have led to increased attention on the topic, notably among practitioners and researchers. This study described and analyzed central retirement transition and adjustment outcomes of ex-professional soccer players from a social sciences and long-term perspective. A total of 78 ex-professionals completed the online questionnaire, most of them having played in the highest German soccer division for several years and having retired from professional soccer 10 years or more ago. Overall, 8.9% (95% CI 2.5 to 21.2; n = 45) showed signs of mental health problems. Compared to the results of a gender- and age-matched sample from the German population, retired ex-professionals were significantly more satisfied with their life and their personal income, and assessed themselves as having a higher subjective social status. Although further evidence is necessary to draw any final conclusion, our results do not point to those publicly discussed concerning central retirement transition and adjustment outcomes of (average) former professional soccer players in the long run.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".