Key Considerations for Advancing Women in Coaching
Bibliographic record
Abstract
Women remain underrepresented in the coaching domain across various levels of sport both in Canada and internationally. Despite the use of mentorship as a key strategy to support female coaches, little progress has been seen in achieving parity. At the same time, greater advances in gender equity have occurred in other non-sport sectors such as business, engineering, and medicine. The purpose of this study, therefore, was to learn from non-sport domains that have seen advances in gender equity to inform mentorship for women in coaching. A mixed-methods methodology was employed and consisted of distributing mentorship surveys to female coaches (n = 310) at various competitive levels, representing current (88%), former (12%), full-time (26%), part-time (74%), paid (54%), and unpaid (46%) coaching status. In addition, eight in-depth semi-structured interviews were also conducted with women in senior-level positions across various non-sport domains, including business (n = 1), media (n = 1), engineering (n = 2), higher education (n = 1), law (n = 1), and medicine (n = 2), regarding the role of mentorship in advancing women in their field. A descriptive and thematic analysis of the survey and interview data were conducted and findings are interpreted to suggest considerable variation in the characteristics of female coaches’ mentoring relationships, as well as the need to move beyond mentorship to sponsorship for advancing women in coaching. Recommendations for future research and advancing women in coaching are provided.
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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 | 0.021 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".