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Record W2772905039 · doi:10.1080/00336297.2017.1386114

Physical Activity in Former Competitive Athletes: The Physical and Psychological Impact of Musculoskeletal Injury

2017· article· en· W2772905039 on OpenAlexaff
Hayley Russell, Jill Tracey, Diane M. Wiese‐Bjornstal, Evan Canzi

Bibliographic record

VenueQuest · 2017
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsCompetitive athletesAthletesPhysical activityPhysical therapyPsychologyPhysical medicine and rehabilitationMusculoskeletal injuryInjury preventionMedicinePoison controlMedical emergencyAlternative medicine

Abstract

fetched live from OpenAlex

Although competitive athletes exceed recommendations for physical activity while they are competing in sport, this does not necessarily translate into regular physical activity after retirement from sport. Research suggested the nature of competitive sport participation may not be conducive to lifelong physical activity. We propose one element of competitive sport participation that may impede physical activity post-retirement is injury. We propose that Vela and Denegar’s model of transient disablement in the physically active with musculoskeletal injuries (DPA) may be appropriate to examine the long-term consequences of sport-related injury—particularly with respect to physical activity disablement. Based on our review of literature, we propose the physical and psychological effects of injuries in sports present unique long-term barriers to physical activity in former competitive athletes. Future research could use the DPA as a foundation for assessing the long-term implications of sport-related injuries—particularly with respect to physical activity.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.404
Teacher spread0.379 · 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 source (direct Gemma or distilled Codex), 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

Citations22
Published2017
Admission routes1
Has abstractyes

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