Development and content validation of the Training Session Evaluation Questionnaire to assist coaches implementing skill acquisition principles in soccer
Bibliographic record
Abstract
Coaching involves designing learning environments based on pedagogical approaches to foster skill emergence and enhance transfer to competition.Despite extensive skill acquisition literature guiding player development, the application of these concepts in high-performance domains requires further exploration and effective knowledge transfer.This study aimed to develop and validate the Training Session Evaluation Questionnaire (TSEQ) to assist coaches in self-assessing training sessions founded on evidence-based skill acquisition principles.The initial development of the TSEQ involved an internal narrative literature review and iterative discussions within the research team to identify relevant principles based on four main criteria: empirical support, ecological validity, practical applicability, and conciseness.The following principles were selected: representativeness, training variability, challenge point framework, training under pressure, shared knowledge and affordances, and video feedback.Twelve experienced coaches (n = 12) participated in this study, which utilized a Delphi approach to assess content validity (CVI) of the questions, evaluate inter-rater consistency using two videotaped training sessions, and collect feedback at each stage to refine the TSEQ.Results demonstrated content validity (CVI ≥ 80%) for 13 out of 14 questions.Fifteen out of 19 coach comments were retained to improve the TSEQ.Inter-rater consistency was achieved in one round with good consistency (SD ≤ 1) for all questions of training session 1, and good (SD ≤ 1; 11 questions) and average (±1-2 SD; 3 questions) consistency for training session 2.The final version of the TSEQ is a valid tool allowing soccer coaches to assess the alignment of their practices with skill acquisition principles and may help bridge theory and practice in coaching.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.054 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".