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Record W4385994511 · doi:10.1136/bmjsem-2022-001516

Call for the application of a biopsychosocial and interdisciplinary approach to the return-to-sport framework of snow sports athletes

2023· article· en· W4385994511 on OpenAlexaff
P. Müller, Jim Taylor, Matthew J. Jordan, Johannes Scherr, Evert Verhagen, Dave Collins, Jörg Spörri

Bibliographic record

VenueBMJ Open Sport & Exercise Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicWinter Sports Injuries and Performance
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBiopsychosocial modelRehabilitationAthletesSnowApplied psychologyPsychologyPhysical medicine and rehabilitationPhysical therapyMedicinePsychotherapistGeography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.538
Threshold uncertainty score0.528

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.383
Teacher spread0.351 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2023
Admission routes1
Has abstractyes

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