Exploring the Context of Coached Masters Swim Programs: A Narrative Approach
Bibliographic record
Abstract
Knowing the psychosocial themes in a specific sport context allows us to understand athletes’ experiences and informs approaches of coaches (Côté et al., 1995) and sport programmers (Danish et al., 2005). Few qualitative studies focus on psychosocial conditions of adult athletes in coached sport settings. The purpose of this study was to capture important psychosocial themes from the perspective of Masters swimmers involved in day-to-day coached swimming environments. Data were collected using semi-structured open-ended interviews with 10 competitive swimmers (5 male, 5 female; M age = 53 years; range: 45-65 years). Analyses revealed four over-arching themes that represented athletes’ a) motives for swimming, b) perspectives on competition, c) experiences specific to being a Masters swimmer, and d) perspectives on being coached. Using a qualitative narrative approach (Denison, 2011), we developed three narrative profiles to depict how our Masters swimmers had different experiences relating to these themes. Discussion focuses on how swimmers’ understanding of the four over-arching themes depends on their profile.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".