Talent inclusion and genetic testing in sport: A practitioner’s guide
Bibliographic record
Abstract
Current scientific evidence does not support the implementation of genetic tests to enhance the processes of talent identification and development systems. Regardless of this consensus, it appears likely that sport stakeholders will continue using genetic tests. This paper aimed to provide practitioners with some best practice guidelines if implementing genetic testing within their organisations. First, we assess the growth and perceived flaws of direct-to-consumer genetic testing companies targeted towards sport. The sports genomic literature is then summarised to demonstrate the lack of established genetic associations with sporting phenotypes and the prevalent limitations that exist in this field of research. Following this, examples are presented suggesting some stakeholders in sport have already used genetic tests to screen for variants associated with performance phenotypes, while the potential appeal of genetic information to sport stakeholders is also discussed. The value of increased genetic literacy (i.e., enhanced education/understanding of genetic information) is then considered, as well as the promotion of talent inclusion (i.e., using genetic tests to include or retain athletes rather than for de-selection and exclusion purposes). To conclude, we offer practitioners several recommendations and best practice guidelines with regards to the implementation of genetic testing in sport.
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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 | 0.027 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.016 | 0.012 |
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".