Predictors of the Use of Performance-Enhancing Substances by Young Athletes
Bibliographic record
Abstract
OBJECTIVES: To document the use of performance-enhancing substances (PES) by young athletes and to identify associated factors. DESIGN: Retrospective survey. SETTING: Self-reported anonymous questionnaire. PARTICIPANTS: Three thousand five hundred seventy-three athletes (mean age, 15.5 years) from Quebec provincial teams run by organizations recognized by the Government of Quebec. INTERVENTIONS: All subjects filled out a validated questionnaire on factors associated with the use of and the intention to use PES. MAIN OUTCOME MEASURES: The use of and intention to use PES. RESULTS: In the 12 months before filling out the questionnaire, 25.8% of respondents admitted having attempted to improve their athletic performance by using 1 or more of 15 substances that were entirely prohibited or restricted by the International Olympic Committee. Multiple regression analyses showed that behavioral intention (beta = 0.34) was the main predictor of athletes' use of PES. Attitude (beta = 0.09), subjective norm (beta = 0.13), perceived facilitating factors (beta = 0.40), perceived moral obligation (beta = -0.18), and pressure from the athlete's entourage to gain weight (beta = 0.10) were positively associated with athletes' behavioral intention to use PES. CONCLUSIONS: This study provides evidence that supports the predicting value of the theory of planned behavior. Results suggest that the athlete's psychosocial environment has a significant impact on the decision to use PES and support the need to integrate this factor into the development and implementation of prevention interventions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".