An exploration of the Role of Mentorship in Advancing Women in Coaching
Bibliographic record
Abstract
Women coaches continue to be underrepresented in the coaching domain (LaVoi, McGarry, Fisher, 2019) despite the growth and advancement of women in non-sport fields (Statistics Canada, 2017). A notable strategy used to develop and advance women in non-sport sectors is mentorship and while mentorship initiatives currently exist for women in the coaching domain, we know little about women coaches’ experiences of mentorship, what they learn and how they develop through mentorship, and how they can be better supported through mentorship to facilitate career advancement. The purpose of this dissertation, therefore, is to explore the role of mentorship in the advancement of women in coaching. A mixed methods approach was used in this study, which led to the production of three manuscripts: 1) Towards a process for advancing women in coaching through mentorship; 2) Benefits of a Female Coach Mentorship Program on women coaches’ development: An ecological perspective; and 3) Key considerations for advancing women in coaching. The findings from these studies provide much needed empirical data on the role of mentorship in women coaches’ growth and advancement, and more specifically, on the importance of process-driven and group-based mentorship for women coaches, the need for greater organizational involvement and macro-level changes in mentoring women coaches, and the need to shift to sponsorship to help advance women in coaching.
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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 | 0.026 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".