What about women coaches? A retrospective examination of barriers and facilitators that affect leadership progression
Bibliographic record
Abstract
Although women and girls are making a space for themselves in sport, the underrepresentation of women coaches and findings within the literature suggests that women continue to challenge the historical connection between gender, power and masculine hegemony (Bougher et al., 2021; Theberge, 1984). Underrepresentation of women in coaching can often be attributed to external, social, and structural barriers, such as hiring from principal similarity, unequal assumptions of competence, microaggressions, homophobia, and a lack of role models (LaVoi et al., 2019; Norman & Simpson, 2022). The purpose of the current study was to understand the women coaches’ journey by exploring the facilitators and barriers that affected their progression in leadership roles within the sport system. Using a retrospective interview procedure in tandem with Côté and colleagues’ (2020) Personal Assets Framework, a spectrum of 13 engaged and 7 disengaged model women coaches were identified and asked to reflect upon their unique leadership experiences. Following a two-phased interview approach, a thematic analysis indicated that intrinsic motivations of growth facilitated coaching career progressions, supportive mentors, and a passion for the sport. Additionally, the coaches discussed many barriers such as doubt from male colleagues, cost of travel, the bias of organizations and colleagues, and being disregarded for positions and opinions.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".