Predicting elite Scottish athletes’ attitudes towards doping: examining the contribution of achievement goals and motivational climate
Bibliographic record
Abstract
Understanding athletes' attitudes to doping continues to be of interest for its potential to contribute to an international anti-doping system. However, little is known about the relationship between elite athletes' attitudes to drug use and potential explanatory factors, including achievement goals and the motivational climate. In addition, despite specific World Anti-Doping Agency Code relating to team sport athletes, little is known about whether sport type (team or individual) is a risk or protective factor in relation to doping. Elite athletes from Scotland (N = 177) completed a survey examining attitudes to performance-enhancing drug (PED) use, achievement goal orientations and perceived motivational climate. Athletes were generally against doping for performance enhancement. Hierarchical regression analysis revealed that task and ego goals and mastery motivational climate were predictors of attitudes to PED use (F (4, 171) = 15.81, P < .01). Compared with individual athletes, team athletes were significantly lower in attitude to PED use and ego orientation scores and significantly higher in perceptions of a mastery motivational climate (Wilks' lambda = .76, F = 10.89 (5, 170), P < .01). The study provides insight into how individual and situational factors may act as protective and risk factors in doping in sport.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".