Towards a process for advancing women in coaching through mentorship
Bibliographic record
Abstract
Female coaches continue to be underrepresented in the coaching domain despite remarkable strides made in female athlete participation. To develop, support, and advance female coaches, mentorship initiatives have been widely recommended. Positive outcomes have been reported in nonsport literature for the professional advancement of women through mentorship, but far less attention has been paid to the advancement of female coaches through mentorship in sport. This study used a multi-methods methodology to explore female coaches’ experiences in, and outcomes of, a female coach mentorship program. Survey data and individual in-depth semi-structured interviews with participating mentor ( n = 7) and mentee coaches ( n = 8) from the program were conducted. Survey data were analyzed descriptively and the interview data were analyzed using an inductive thematic analysis. Findings revealed two primary forms of mentoring support provided through the mentorship program that facilitated personal and professional outcomes for participating mentor and mentee coaches, as well as various quality attributes of the mentorship process. Based upon these findings, a mentorship model for advancing women in coaching is proposed.
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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 | 0.027 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| 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".