Prevalence of Neuromyths Among Canadian Sports Coaches: Implications for Coach Education
Bibliographic record
Abstract
This study explored the prevalence and common sources of pseudoscientific beliefs among sports coaches, particularly those related to learning and brain function. Employing an observational design, we administered a one-time questionnaire to 1,568 Canadian sports coaches recruited via non-probability sampling. Coaches were surveyed regarding their familiarity with neuromyths, their beliefs about general statements concerning brain function, and the influence these ideas have on their coaching practices and the quality of coaching education. Findings indicated that coaches agreed with approximately 58.8% (±19.8) of neuromyth-related statements and accurately identified only 23.1% (±19.0) of scientifically accurate assertions about the brain. The primary exposure to these neuromyths was identified as formal coach education courses. Regression analysis revealed that knowledge of scientifically valid brain-related statements significantly predicted coaches' susceptibility to neuromyths. However, coaches' beliefs in neuromyths did not significantly predict their likelihood of applying brain-based learning ideas in their practical coaching. These results suggest a substantial challenge for sports coaches in differentiating scientifically valid information from pseudoscientific ideas regarding learning and brain functions. Given the potential negative impacts on coaching quality and athlete learning, the study underscores an urgent call for evidence-based reforms in coach education to address and mitigate the spread of pseudoscientific beliefs.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".