The ‘selves’ in doping and its psychosocial mechanisms: harmonised multi-country evidence from high-performing athletes in the UK, US, and China
Bibliographic record
Abstract
BACKGROUND: Doping remains an ongoing threat to clean competition. To date, global preventive initiatives have not addressed critical psychosocial antecedents of doping thoroughly due to the scarcity of knowledge regarding its psychosocial mechanisms from a harmonised cross-country perspective. We, therefore, conducted a multi-country investigation testing the interplay of two important yet overlooked attributes, namely narcissism and self-compassion, and examined their psychosocial mechanisms underpinning doping. METHODS: Using a sample of 499 high-performing athletes (80% competing at national level or above) from the UK, US, and China, we performed a series of multi-variate multi-group moderation models to test the narcissism × compassion interaction on doping and examined potential psychosocial mechanisms underlying such effects. In all analyses, we applied cluster control to adjust for coach-/team-level confounds and compared fixed vs. random effects models for cross-country comparisons. RESULTS: We found an identical interaction across study countries showing self-compassion alleviates narcissism-related doping willingness (especially that vulnerable narcissism drives). Grandiose narcissism's protective effect on vulnerable narcissism-related doping moral disengagement was invariant across countries. Resilient coping appears to be a consistent mechanism across study countries that explains narcissism-related risk and compassion-related protection. Fear of failure manifested varied mechanistic effects in different study countries, inferring potential cross-cultural differences. CONCLUSION: Vulnerable narcissism is a critical person-level correlate of doping. Grandiose narcissism, in the presence of self-compassion, can alleviate such risk thanks to enhanced resilient coping. Future education and interventions should tackle these important person-level attributes for anti-doping.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".