‘I feel more comfortable in contact with similar-sized players’: male youth rugby union players’ perceptions of bio-banded training
Bibliographic record
Abstract
Background Bio-banding has been used to moderate disparities in maturity status between youth players of the same chronological age to support development.Aim The purpose of this study was to investigate male youth rugby union players’ perceptions of bio-banded training compared to chronological age grouping.Subjects and methods Fifty-four U16 players participated in games-based bio-banded training before completing an eight-item Likert scale questionnaire, with the option of providing an explanation for each response. Aligning with typical peak height velocity (PHV) categories, maturity banding was based upon percentage of predicted adult height (i.e. circa-PHV ≤95% and post-PHV ≥96%). A series of one-sample and Welch’s t-tests were used to analyse quantitative data, and qualitative data were assessed using an inductive thematic analysis.Results Irrespective of maturity status, the majority of players perceived they understood and enjoyed most aspects of the bio-banding format and believed they were less likely to get injured compared to chronological age grouping. Both maturity groups perceived bio-banding facilitated superior psychosocial capacities and offered a greater technical and tactical challenge, whilst circa-PHV and post-PHV players perceived bio-banding as less and more physically challenging than chronological age grouping, respectively.Conclusion The results suggest using bio-banded grouping for training, as an adjunct to chronological age grouping, may positively contribute to the holistic development of male youth players in rugby union irrespective of maturity status.
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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 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".