Optimising long-term athletic development: An investigation of practitioners’ knowledge, adherence, practices and challenges
Bibliographic record
Abstract
Long-term athletic development practices have been recommended for the past two decades. However, limited research exists exploring the knowledge and skills required by practitioners to optimise long-term athletic development. Therefore, this study aimed to evaluate the knowledge, adherence, practices, and challenges of practitioners responsible for delivering long-term athletic development. A mixed methods survey was completed by 236 practitioners (e.g., sport coaches, physical education teachers) consisting of four parts; 1) demographics, 2) knowledge, 3) adherence, and 4) practices and challenges. Quantitative and qualitative data were analysed by Friedman's analysis of variance and thematic analyses, respectively. Quantitative findings showed practitioners 1) recognised their responsibility for delivering long-term athletic development, 2) have a familiarity with existing developmental models, and 3) had high adherence, focused upon health and wellbeing, to delivering long-term athletic development. However, practices associated with growth and maturity, monitoring and assessment, and the systematic progression and individualisation of training had lower adherence. Qualitative analysis indicated that practitioner's perceived definitions of athleticism and long-term athletic development were inconsistent, especially according to the psychological components (i.e., confidence). Practitioners' descriptions of their long-term athletic development practices identified two higher order themes; 1) goals, in which long-term athletic development "is for life" and the importance of "an individual centered journey" highlighted as sub-themes; and 2) realities of delivering long-term athletic development, whereby variety in programme delivery, monitoring development and practical challenges were noted as key priorities. Eight practical challenges were identified including governance and priorities, resources, education, early specialization, high training volumes, staff communication, parents and youth motivation. This mixed method survey highlighted a multitude of knowledge, adherence, practices and challenges towards long-term athletic development. These novel findings can help inform policy to optimise long-term athletic development and to support the complex problem of developing a healthier, fitter and more physically active youth population.
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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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".