Giving back: investigating the motives of female volunteer youth sport coaches
Bibliographic record
Abstract
Despite the many benefits of youth sport, the recruitment and retention of coaches is an ongoing pervasive issue faced by sport organizations. This lack of coaches is even more prevalent among females when compared to their male counterparts despite the significant increases in females participating in sport. Nevertheless, relatively little research has been conducted on the notion of “giving back” to sport as a coach, particularly with female sport coaches. Therefore, the purpose of this study was to utilize a grounded theory approach to explore the experiences of female coaches to identify key factors that have driven them to contribute to their sport organizations through coaching. Eleven Canadian female youth sport coaches participated in semi-structured interviews to investigate their own youth sport experiences, transition from athletic to coaching role, and current role as a coach. In line with a grounded theory approach, data collection and analysis followed a semi-iterative cycle including theoretical sampling which informed changes to the interview guide based upon previous interviews. Findings show that coaches’ motivations to contribute to sport fall under three categories: 1) deriving meaning from their coaching role; 2) enjoying their position; and 3) feeling moral obligation to give back to sport. These common motivators appear to outweigh common barriers of gender and age that arose as unique to the female experience. Results map onto similar literature as female coaches also strive to be the female role models they once had in sport, and gender stands in the way of many female coaches.
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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 | 0.004 | 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 | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".