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Record W4398248343 · doi:10.1519/jsc.0000000000004789

A Biopsychosocial Model for Understanding Training Load, Fatigue, and Musculoskeletal Sport Injury in University Athletes: A Scoping Review

2024· review· en· W4398248343 on OpenAlexaff
Zachary J. McClean, Kati Pasanen, Victor Lun, Jonathan Charest, Walter Herzog, Penny Werthner, Amanda M. Black, Reid Vander Vleuten, Elise Lacoste, Matthew J. Jordan

Bibliographic record

VenueThe Journal of Strength and Conditioning Research · 2024
Typereview
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsUniversité LavalCanadian Sleep & Circadian NetworkUniversity of Calgary
Fundersnot available
KeywordsBiopsychosocial modelAthletesMusculoskeletal injuryPhysical therapyApplied psychologyInclusion (mineral)MedicinePsychologyPhysical medicine and rehabilitationAlternative medicinePsychiatrySocial psychology

Abstract

fetched live from OpenAlex

ABSTRACT: McClean, ZJ, Pasanen, K, Lun, V, Charest, J, Herzog, W, Werthner, P, Black, A, Vleuten, RV, Lacoste, E, and Jordan, MJ. A biopsychosocial model for understanding training load, fatigue, and musculoskeletal sport injury in university athletes: A scoping review. J Strength Cond Res 38(6): 1177-1188, 2024-The impact of musculoskeletal (MSK) injury on athlete health and performance has been studied extensively in youth sport and elite sport. Current research examining the relationship between training load, injury, and fatigue in university athletes is sparse. Furthermore, a range of contextual factors that influence the training load-fatigue-injury relationship exist, necessitating an integrative biopsychosocial model to address primary and secondary injury prevention research. The objectives of this review were (a) to review the scientific literature examining the relationship between training load, fatigue, and MSK injury in university athletes and (b) to use this review in conjunction with a transdisciplinary research team to identify biopsychosocial factors that influence MSK injury and develop an updated, holistic biopsychosocial model to inform injury prevention research and practice in university sport. Ten articles were identified for inclusion in this review. Key findings were an absence of injury surveillance methodology and contextual factors that can influence the training load-fatigue-MSK injury relationship. We highlight the inclusion of academic load, social load, and mental health load as key variables contributing to a multifactorial, gendered environmental, scientific inquiry on sport injury and reinjury in university sport. An integrative biopsychosocial model for MSK injury in university sport is presented that can be used to study the biological, psychological, and social factors that modulate injury and reinjury risk in university athletes. Finally, we provide an example of how causal inference can be used to maximize the utility of longitudinally collected observational data that is characteristic of sport performance research in university sport.

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)0.004
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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.753
Threshold uncertainty score0.484

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.208
GPT teacher head0.470
Teacher spread0.262 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations37
Published2024
Admission routes1
Has abstractyes

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