How high-performance practitioners think and intervene to change technique
Bibliographic record
Abstract
OBJECTIVE: Changing technique, whereby a permanent strategy is adopted to improve performance or reduce injury risk, is a common goal among sport practitioners in high performance sports. However, there is little information about how technique change is done in practice and whether any evidence-based methods or frameworks are applied. Our aim was to explore individual and team-sport practitioner experiences concerning the process and types of methods adopted for technique change, across a range of practitioner groups and sports. DESIGN: Qualitative interviews were conducted with fifteen practitioners from three practitioner groups; sport coaches, therapists, and strength & conditioning coaches (S&C), who had experience changing technique with adults in high-performance sports' settings. METHODS: Data was thematically analyzed guided by a qualitative approach, apriori knowledge on the topic, and a critical realist paradigm. RESULTS: When intervening, S&Cs and therapists reported focusing on physical assessments and modifications, often divorcing the action from the sport context initially. For example, jump mechanics were assessed on a force plate, followed by jump exercise interventions in isolation, then final applications to the sport and positional demands, such as a header in soccer, were implemented. Coaches reported staying within the sport context, but scaling back task difficulty. There was little evidence that techniques discussed in the literature, such as exaggerating errors, or contrasting between old and new ways, were used to change technique. Practitioners primarily used prescriptive, direct instructional approaches, using feedback to highlight 'errors' (focusing attention both internally and externally). They emphasized a practitioner integrated approach for a successful intervention, but there was a lack of objective assessment of an intervention's success, particularly in transfer to competition. CONCLUSION: Sport practitioners were regularly working on technique change in a systematic way, but not guided by current evidence-recommended methods. There was a desire to learn more about these methods, particularly those that seemed counter to current direct instructional practice; such as continual contrasting and augmenting of existing 'errors'. There is a need for greater communication between researchers and practitioners to facilitate awareness of methods and the rationale behind their effectiveness.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".