Measuring Outcomes of Swimmers' Non-Regulation during Practice: Relationships between Self-Report, Coaches' Judgments, and Video-Observation
Bibliographic record
Abstract
The purpose of this investigation was to further validate measures for three behavioral items that coaches claimed to reflect outcomes of non-regulation by swimmers during training. Measures for 33 competitive swimmers were collected for behaviors that reflected a lack of motivation to comply with a coach's full training volume prescription in (1) warm-up, and in the (2) remaining workout, as well for (3) the off-task durations of swimmers in warm-up. Measures were collected concurrently via self-report and video-observation at nine practices across five weeks of training. Coaches rated swimmers for levels of (a) on-task behavior in warm-up, and (b) initiative, motivation and discipline in the workout. Self-reported and observed measures were separately contrasted with low and high motivation groups, based on coach ratings. Results showed that when coaches rated swimmers low on self-regulated behaviors the swimmers had in fact completed less of the prescribed swim volume. Likewise, swimmers who spent a lot of time out-of-stroke in warm-up were not self-regulated, according to coach ratings. In a supplementary analysis, self-reported measures for missed volume during warm-up were contrasted with veridical measures obtained by video observation for a sub-set of 16 swimmers. Findings indicated that self-report at the end of workout was inaccurate and/or biased due to distorted memory processes and self-presentation influences. Discussion focused on the future use of valid and observable measures representing outcomes of non-regulation in applied sport 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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".