The effect of a brief equity, diversity, and inclusion training module on fitness professionals' racial bias
Bibliographic record
Abstract
Many individuals from racialized groups experience stigma and bias from their healthcare practitioners and fitness professionals. Small Steps for Big Changes (SSBC) is a diabetes prevention program designed to empower individuals at risk of type 2 diabetes to make diet and exercise changes. SSBC is delivered in the community by fitness professionals, who take training to become SSBC coaches. SSBC training presents an opportunity to educate fitness professionals on equity, diversity, and inclusion (EDI). This study investigated whether a brief EDI module could reduce fitness professionals' racial biases. Twelve participants read an SSBC mock client vignette that stated the client's race. Participants then completed a questionnaire assessing their perceptions of the client presented in the vignette and their empathetic awareness towards people who experience racial biases. The participants were then randomized to watch either the SSBC EDI module (EDI-group) or a time-matched neutral video (non-EDI-group). All participants then repeated the client perceptions and empathetic awareness measures. The non-EDI-group was used as a manipulation check. Perceptions of the client increased pre- (M = 5.07±0.47, p = 0.05) to post-module (M = 5.39±0.72) in the EDI-group, but not in empathetic awareness (p = 0.85). These findings suggest that a brief EDI module can reduce fitness professionals' racial bias.Acknowledgments: I would like to respectfully acknowledge that this research was conducted on the lands of the Syilx Nation. This research was funded by the Stober Foundation and CIHR grant (Jung) #020438.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".