SUICIDES OF FAMOUS ATHLETES OF MODERN TIME: STORIES AND REASONS
Bibliographic record
Abstract
Introduction. Professional athletes often suffer from physical and psychological injuries, they are underconsiderable pressure due to the constant need to demonstrate their victories, the high level of competition, violations of fair play, etc. These factors can increase the risk of suicide among them.The aim of the work is to study the life, professional career, reasons and methods of suicide of famous athletes in the first quarter of the 21st century. We used general scientific (description and observation) and theoretical (analysis, synthesis, classification, explanation, and generalization) research methods to achieve the goal.Results. The present study analysed information on 336 male and 45 female athletes who committedsuicide between January 2000 and December 2024. All the data was taken from verified sources. The research demonstrated that for every instance of female athlete suicide, there were approximately nine instances of male athletes committing suicide. The highest number of male athlete suicides was recorded in 2019, the lowest in 2000, and the highest number of female athletes committed suicide in 2020. Since 2009, there has been an increase in the incidence of suicidal acts. A study revealed that male athletes were most prone to committing suicide between the ages of 30 and 49, while female athletes demonstrated a peak in suicide rates between the ages of 20 and 39. In the case of the 252 athletes whose method of suicide is known, hanging and gunshot wounds were the most prevalent. In the case of the 26 female athletes, the most prevalent underlying factors contributing to injury were found to be falls from a height and overdosesor poisoning with dangerous substances. It was not possible to ascertain the method of suicide of 84 male athletes and 19 female athletes due to an absence of relevant information.Research has identified a number of factors that have been demonstrated to increase the risk of suicide, including diseases and injuries, career development and achieved results, difficult social relationships and economic reasons. The behaviour of an athlete who is prone to cruelty, sudden mood swings, depression,and suicidal intentions always requires urgent action. In such a state, the individual’s actions can become a potential threat not only to themselves but also to their loved ones.Conclusions. In the contemporary context, the development of reliable algorithms for predicting and preventing suicide has become imperative. The identification of problems at an early stage and the prescription of appropriate treatment are of paramount importance. Furthermore, restrictions on access to firearms must be considered, along with the prevention of the transformation of suicide locations into attractive sites for the development of than atourism.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| 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".