Call for the application of a biopsychosocial and interdisciplinary approach to the return-to-sport framework of snow sports athletes
Bibliographic record
Abstract
Snow sports such as alpine skiing or snowboarding are associated with a high risk of injury and reinjury and are subject to a very special environment with specific rehabilitation challenges that must be addressed. Due to geographic decentralisation, seasonal climatic limitations, alternation of training in off-snow and on-snow settings and unique loading patterns of practising these sports, special rehabilitation structures and processes are required compared with other sports. In addition, returning to preinjury performance requires a high level of confidence and a resumption of risk-taking in demanding situations such as high-speed skiing and high-amplitude jumps. A biopsychosocial and interdisciplinary approach can be viewed as a holistic, athlete-centred approach that promotes interprofessional communication and collaboration. This is particularly central for managing the physical/biological, psychological and social demands of injury management for snow sports. It can help ensure that rehabilitation content is well coordinated and tailored to individual needs. This is because transitions between different rehabilitation phases and caring professionals are well aligned, and rehabilitation is understood not only as purely 'physical recovery' but also as 'psychological recovery' considering the snow sports-specific setting with specific social norms. Ultimately, this may improve the rehabilitation success of snow sports athletes.
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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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.011 | 0.030 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.008 | 0.021 |
| Insufficient payload (model declined to judge) | 0.009 | 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".