Trajectories of performance and motivation for competitive swimmers
Bibliographic record
Abstract
This study investigated the longitudinal trajectories of swimmers' competitive performance results, as well as the motivational predictors of the emerging trajectories. XXX swimmers were followed for seven swimming seasons (2010 – 2016). In 2010, athletes provided background characteristics (age, gender, years of experience) and completed self-determination theory-based measures of sport and general motivation. Their top score from each season (2011 – 2016) were then used to track their performance trajectories. The data were analyzed using latent growth mixture modeling to determine the best-fitting model of performance trajectories and the motivational and background characteristics were explored as predictors using multinomial logistic regression. The analyses revealed five similar latent classes: Dropout, Delayed Dropout, Stable, Improvement, and Greatest Improvement. Analyses indicated that the background characteristics did not differ between trajectories; however, members of the dropout and delayed dropout groups reported higher controlled motivation and amotivation for sport at baseline, where the dropout group displayed higher levels than the delayed dropout group. Autonomous motivation towards sport was not associated with any trajectories; however, general autonomous motivation was positively associated with the improvement and large improvement groups, where athletes in both trajectories reported more autonomous general motivation than the other trajectories. The results found support for trajectories for athletes' where some dropped out immediately, or within a couple of years. The remaining athletes kept swimming, but some demonstrated consistent performances, and others improved somewhat or a lot. These results support that motivation not only predicts athletes' persistence within their sport, but also their potential improvement.
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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 | 0.004 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".