Towards a process for advancing women in coaching through mentorship
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it