Coaches' Perceptions of Non-Regulated Training Behaviors in Competitive Swimmers
Bibliographic record
Abstract
Athletes who fail to self-regulate are less disciplined and motivated, show less initiative, and fail to maximize opportunities for acquisition during training. This investigation attempted to identify a short list of behaviors that swim coaches recognized as indicators of non-regulation by their swimmers during training. In Study 1, five coaches described the behavior of swimmers in various contexts during interviews. Qualitative analysis of interviews resulted in two lists of 28 activities that characterized self-regulation and non-regulation, respectively. In Study 2, two different samples of coaches (n=18; n=16) rated the items identified in Study 1 for how well they represented self-regulated and non-regulated training behaviors among swimmers. Based on inclusion criteria, two lists of 28 items were shortened to one list of seven non-regulated training habits, including: poor attendance; off-task in warm-up; incomplete volume in warm-up; incomplete volume for the entire workout; inaccurate recall of pace times; last to arrive on deck; and lack of focus during kick sets. The authors discuss the relevance of an observational checklist for helping coaches identify athletes in need of remedial self-regulatory strategies, as well as how measures for these items may be employed in intervention research.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".