Incentives and deterrents for drug-taking behaviour in elite sports: a holistic and developmental approach
Bibliographic record
Abstract
Research question: In order to gain a better understanding of the key decision factors that lead some athletes to use doping and others to stay clean, this study used the Push Pull Anti-push Anti-pull framework and the Holistic Athletic Career model as theoretical frameworks in order to capture the complex nature of this decision process.Research methods: Multiple qualitative methods (i.e. face-to-face interviews, focus group interviews, biographical analyses) were used to explore the perspectives of 36 Dutch-speaking Belgian (former) elite athletes, 5 elite coaches, 4 doping ‘experts’, and 3 self-admitted doping users. Data were analysed using deductive content analysis.Results and findings: Incentives as well as deterrents for doping use, including both current factors and perceived future risks or benefits, were found at different levels of athletes’ development (i.e. athletic, psychological, psychosocial, financial, and policy levels). Furthermore, the decision to use doping was found to be related to critical points during athletes’ career.Implications: Detailed insight into the complex decision whether or not to use doping can assist stakeholders in high performance management in the development of preventive anti-doping strategies.
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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 | 0.002 | 0.004 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| 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".