Doping in elite cycling: a qualitative study of the underlying situations of vulnerability
Bibliographic record
Abstract
Doping is considered a critical deviant behavior in competitive sports, and particularly in cycling, even though the phenomenon remains limited in sports in general. Previous qualitative studies have contributed to identify situations of vulnerability to doping in athletes. However, much of the research tends to focus on singular dimensions of vulnerability, such as physical or psychological aspects. The present study aimed to extend existing knowledge by concurrently exploring and attempting to categorize different types of situations of vulnerability that predispose elite cyclists to engage in doping. Ten high-level French-speaking doped cyclists were recruited (Mage = 49; SD = 14.63, two women). Semi-structured interviews were conducted. Both deductive and inductive thematic analyses were performed. Our results highlighted four types of vulnerability situations: (a) psychological (e.g., negative affects, maladaptive motivation, depression), (b) physical (e.g., exhaustion, impairments, injuries), (c) relational (e.g., organized doping, control, psychological or sexual harassment, social approval of doping), and (d) contextual (e.g., cycling culture, weather conditions, competitive stakes). By providing a clearer categorization of the situations of vulnerability that converge toward doping in sport, this comprehensive study allows for a holistic understanding of the various vulnerabilities. It paves the way for future research on related vulnerabilities and dispositional factors. Practically, it should also improve doping screening and prevention, and provide more favorable conditions for 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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".