Embrace your discomfort: leadership and unconscious bias in sport and exercise medicine
Bibliographic record
Abstract
Unconscious bias is present at all levels of society. It exists within our Sport and Exercise Medicine (SEM) community and is detrimental to the specialty and those it serves. Many may be unaware of how unconscious bias has impacted their career trajectory, and that of their peers. This editorial explores the concept of unconscious bias and prompts actions to initiate meaningful change. The human brain is complex, programmed to make quick judgements about people and situations based on visual, verbal and behavioural cues.1 Over time, unconscious pathways form between these cues and how we judge them, gaining strength with each unchallenged repetition. These habituated norms can leave us feeling unsettled when we experience something outside of our expectations. This is referred to as unconscious bias, a consequence of learnt stereotypes deeply ingrained within our beliefs, influencing the way we automatically and subconsciously engage with people and situations. The nature of our individual bias is nurtured from childhood through cultural conditioning, media portrayals and upbringing.2 ### Issues of unconscious bias in SEM While conscious bias has created, and overtly contributes to, many of society’s inequalities, unconscious bias is covert, overlooked and too often unaddressed. Unconscious bias has perpetuated inequality resulting in certain groups having fewer rights, privileges and less power than others.3 It is ubiquitous, and the implications within SEM communities are significant, resulting in …
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".