Are Athletes' Doping-Related Attitudes Predicted by Their Perceptions of Coaches' Confrontation Efficacy?
Bibliographic record
Abstract
BACKGROUND: The coaching process is complex and dynamic. Coaches are known to affect athlete attitudes indirectly, through athletes' perceptions and interpretations. This process has been verified with a variety of ethical behaviors. OBJECTIVES: The current study investigated the relationships between athletes' perceived doping confrontation efficacy of their coaches and athlete's anti-doping attitudes. METHODS: A sample of athletes (N = 96) completed a version of the Doping Confrontation Efficacy Scale that was modified to assess their perception of their coaches' anti-doping confrontation efficacy, and the PEAS (Performance Enhancing Attitude Scale), which assessed their performance-enhancing attitudes. RESULTS: The results of a multiple linear regression model showed that perceived confrontation efficacy significantly predicted athletes' anti-doping attitudes. Specifically, the factor of efficacy with respect to intimacy was the sole significant predictor. CONCLUSIONS: These results show that a pattern of athletes' interpretation of coaches' values is related to their own attitudes, much like with cheating and sportsmanship. This finding is consistent with the conceptualization of confrontation in general independent of 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".