Participation profiles of masters swimmers: Who are they, and how did they get here?
Bibliographic record
Abstract
Research highlights the heterogeneity of masters swimmers in terms of motives, needs, competitiveness, or sociability (Callary, Rathwell, & Young, 2015), and pathways into masters sport (Larson, McHugh, Young, & Rodgers, 2018). More robust understanding of masters swimmer profiles could enhance programming and coaching tailored to these specific groups and guide our understanding of sport for life. This study aimed to identify profiles of adult swimming participants, and associations these profiles had with demographic variables and factors relating to transitions into masters swimming. Survey data were collected from 205 Canadian swimmers with previous competitive youth swimming experience (M age = 44.4, range = 18-85; 60% women, 40% men). Two-step clustering analysis was conducted using five variables: total sport involvement (number of sports yearly), whether they considered swimming their main sport, season duration (months), swim practices attended weekly, hours of swim practice weekly; swim meets attended yearly. Three distinct profiles emerged: 1) Specializers, 2) Competitors, and 3) Samplers. These profiles differed significantly from one another on all of the variables except season duration. Next, we examined differences on demographic variables by profile. No significant differences emerged on age, gender, number of children, or marital status. Competitors had slightly lower educational attainment compared to the other profiles. Finally, we examined their transitions into masters swimming, considering the amount of time off following youth swimming and their age of registering in masters swimming. No significant differences emerged. Further research should look to connect the quality and intensity of youth experiences to masters' current status.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".