An empirical model of athlete decisions to use performance‐enhancing drugs: qualitative evidence
Bibliographic record
Abstract
Models of athlete decisions to use performance‐enhancing substance and method (PESM) lack an empirical base. In this paper, the validity of the content (variables thought to influence use) and process (how the variables come together) of these models is assessed. Reporting the second qualitative stage of a broader choice modelling study, n = 20 interviews (conducted from August 2007 to January 2008) and three follow‐up focus groups (n = 29; June 2008) with athletes, coaches, sports nutritionists, physiotherapists, sports administrators and sports scientists were used to generate a grounded model of athlete PESM use. Ten factors, organised around four themes, emerged (objective of PESM use, about the PESM, the deterrence system and consequences if prosecuted). The model suggested by these factors provides confidence in terms of what variables influence athlete PESM use (content), although questions remain as to whether rationality reflects how the behaviour manifests. This latter point remains to be tested in the third quantitative stage of this research programme.
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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 | 0.070 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".