"It Should Be My Responsibility": The Invisible and Inspirational Labor of Women Coaches' Mentorship
Bibliographic record
Abstract
Mentorship is gaining popularity for its ability to support the personal and career advancement of aspiring women coaches. Mentoring is commonly accepted as a duty of a coach despite it being a resource-intensive task that often goes underacknowledged. The purpose of this study was to explore the experiences of women coaches who served as mentors in formal Canadian women in coaching mentorship programs. Semistructured interviews were completed with 10 women coaches. A reflexive thematic analysis underpinned by a postfeminist lens revealed two major themes: (a) mentorship as a catalyst for change by those within the system and (b) the invisible costs of promoting change through mentorship. Findings provide important implications for how the sport system can better support women coach mentors to improve their experiences and increase their retention for the future. Ultimately, this paper aims to shift sport researchers' and practitioners' perspectives on the promotion of women coaches' engagement in extraneous roles (e.g., mentoring) to kick-start conversations on creating more equitable working conditions and structures for women coaches within sport.Accepted author manuscript version reprinted, by permission, from Women in Sport and Physical Activity Journal, 2025, 33 (1): , https://doi.org/10.1123/wspaj.2025-0013. © Human Kinetics, Inc. Deposited by shareyourpaper.org and openaccessbutton.org. We've taken reasonable steps to ensure this content doesn't violate copyright. However, if you think it does you can request a takedown by emailing help@openaccessbutton.org.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.034 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.006 |
| 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".