Psycho-social considerations for coaching masters swimmers for competition
Bibliographic record
Abstract
Master athletes (MAs) are adults over 35 years old, who formally register for sport, and acknowledge a degree of competitiveness in their orientation (Young, Callary, & Niedre, 2014). Coaches are integral resources that help to sustain involvement in sport. However, the impact of coaches on MAs’ experiences in sport is unclear. The purpose of this presentation is to qualitatively explore coaches’ behaviours and strategies that help MAs in the lead up to competition, as well as during and after competition. Participants were five female and five male competitive swimmers (from 45-65 years of age) who train 2.5-7 hours per week and work with a coach at least twice per week. MAs were interviewed individually about their recent experiences with coaches and prompted to give examples from their personal experiences to illustrate their specific needs, preferences, and dislikes regarding interactions with coaches. Data were analyzed using interpretative phenomenological analysis (Smith, Flowers, & Larkin, 2009). Each interview was first analyzed separately to find emerging themes before examining across interviews. Results indicate that coaches encouraged MAs to register for competitions, helped with their registration for events, and refined the structure of training in strategic ways for MAs to prepare them for upcoming competitions. Further, during and immediately following competitions, MAs’ experiences with coaches were revealed in terms of how coaches played unique roles with respect to: emotional support; providing performance-enhancing feedback; sharing information about on-site logistics; post-race debriefing; celebrating athlete accomplishments; and how the coaches, themselves, competed. Findings are framed against the backdrop of a dearth of research about coaching MAs with specific conversations devoted to novel considerations for what MAs want from their coaches such as providing basic rules about competition, supporting athletes, and uniquely, competing against their own athletes. Acknowledgments: The researchers would like to thank Cape Breton University's office of research for funding this study. References: Smith, J. A., Flowers, P., & Larkin, M. (2009). Interpretative phenomenological analysis: Theory, method, and research. Los Angeles, CA: Sage. Young, B.W., Callary, B., & Niedre, P.C. (2014). Exploring novel considerations for the coaching of Masters athletes. International Sport Coaching Journal, 1(2), 86-93.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".