Early specialisation in young gymnasts: a mixed methods protocol
Bibliographic record
Abstract
Over the past few decades, we have seen an increase in the popularity of organised sports among youth, especially gymnastics. Along with this increase in the popularity of organised sports, sports specialisation is also on the rise. Some argue that specialising as early as possible is essential for better performance and skill development. However, it has been associated with negative mental health effects in children and adolescents, as well as a higher risk of overuse injuries in young athletes. Although sports specialisation has been a popular research topic in recent years, many questions regarding its underlying factors and its impacts (positive or negative) on injuries, psychosocial health and performance remain unanswered. The purpose of this manuscript is to present the protocol for a study that aims to describe sports specialisation in young gymnasts and to understand the association between sports specialisation, injury, psychological health and physical performance. This study will use a convergent mixed methods design. There will be a qualitative phase where we will conduct interviews and focus group discussions with athletes and different actors in the field. This will be conducted alongside a prospective cohort study over an entire sport season. Athletes will be assessed at the start, middle and end of the season for skill acquisition (core strength and endurance, power and dynamic postural control), psychosocial variables (stress, personality traits, anxiety, and quality of life) and sports participation. They will be followed throughout the season using online weekly surveys to monitor training load and injuries.
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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 | 0.065 | 0.037 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.059 | 0.010 |
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".