A Case Study Situating a Self-Regulated Learning Survey in Consultation Between an Elite Athlete and a Sport Psychology Practitioner
Bibliographic record
Abstract
Athletes can use self-regulated-learning (SRL) competencies to improve their engagement in practice through awareness and control of their cognition, motivation, affect, behavior, and environment. While sport research has extensively developed measures to assess SRL, little work has considered how those tools may facilitate contextually specific interventions in applied practice. In an instrumental case study, supported by a semistructured interview, we explored how the Polish Short Form of the Self-Regulated Learning for Sport Practice (SRL-SP) could be used as an intrapersonal development tool to guide an applied consulting session with an elite athlete. The results exemplify how an SRL survey could be used in consulting to help athletes develop proactive approaches to practice. Specifically, the Polish Short Form SRL-SP was useful as a dialogue tool and for monitoring the development of SRL competencies. The survey’s applied utility may be influenced by characteristics of the discipline, competitive level, and situational coaching context.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".