Managing Diversity to Provide Culturally Safe Sport Programming: A Case Study of the Canadian Red Cross’s Swim Program
Bibliographic record
Abstract
This article examines the piloting of a cultural safety training module in the Canadian Red Cross’s (CRC’s) Water Safety Instructor Development Program. Thematic analysis of interviews with program participants and facilitators revealed two main themes: Inclusion is important and valued by instructors, and accommodation for cultural and ethnic diversity is difficult to achieve in aquatics settings. Doherty and Chelladurai’s (1999) framework was used to understand the strengths and weaknesses of the pilot module. In conclusion, the authors propose that cultural safety training for the instructors alone will not lead to the provision of culturally safe sport; rather, there needs to be a change in the overall organizational culture in which the CRC’s programs are offered if they are to succeed. These findings make three contributions to the literature. First, the authors bridge the existing bodies of literature on critical Whiteness theory and sport management literature that addresses the management of diversity. Second, the authors explore the novel application of cultural safety training for instructors of a sport program. Finally, the authors offer recommendations to enable the development of an organizational culture that is facilitative and supportive with respect to inclusion (i.e., is welcoming) and accommodation (i.e., is flexible and adaptable) of cultural and ethnic diversity in aquatics programming.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".