Keeping Girls in Sport: A Two-Part Evaluation of an E-Learning Program for Coaches and Activity Leaders
Bibliographic record
Abstract
The purpose of this study was twofold: first, to identify who engaged with the Keeping Girls in Sport e-learning program and, second, to evaluate coach and activity leaders’ perceptions of the program and their perceived learnings gained from completing the program. An explanatory sequential mixed-method design was adopted. First, an online survey was distributed to all individuals who had participated in the program. In total, 511 (33% response rate) completed the survey. Quantitative survey data were analysed using descriptive statistics. Subsequently, interviews were conducted with 20 survey respondents. A realist logic of analysis was applied to the qualitative data, and context–mechanism–outcome configurations were formed. Overall, survey findings indicated that most participants identified as women (56%), coaches (69%), and were between 40 and 49 years of age (37%). In general, participants had positive perceptions of the program. Participants perceived that the accessibility and flexibility of the program increased opportunities to engage with content and, thus, their learning. They described improvements in knowledge and perspective regarding working with female athletes. This increase in knowledge provided participants with confidence to establish trusting and positive relationships with others, specifically parents. Nevertheless, participants highlighted a need for more tailored but also more expansive programs.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".