The relative importance of training volume and coach autonomy support for preventing youth swimming attrition
Bibliographic record
Abstract
There are hypothesized associations between high training volume in youth sport and negative psychological and behavioral outcomes such as decreased enjoyment, and increased burnout and dropout. Autonomy support, however, is associated with positive motivational and behavioral outcomes. The purpose of this study was to concurrently explore the relationships of training volume and perceived coach autonomy support with enjoyment, commitment, burnout symptoms, and dropout from swimming. Survey data were collected from 265 swimmers (Mage = 13.78 ± 1.60) representing more than 50 clubs across Canada. Their parents provided training volume data. Several months later, at the start of the next swimming season, a follow-up survey identified which swimmers dropped out. Structural equation modeling did not show a significant relationship between training volume and enjoyment, but there was a significant pathway from autonomy support to enjoyment, which predominantly predicted functional commitment. Obligatory and functional commitment differentially predicted burnout and intentions to continue swimming. Swimmers who dropped out had significantly lower training volume, enjoyment, functional commitment, and intentions to continue swimming, and higher sport devaluation, compared to those who continued swimming in the following season. Perceptions of an enjoyable, autonomy-supportive training context in adolescent swimming seem to have greater associations than training volume with several psychological and behavioral outcomes, including burnout symptoms and dropout.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".