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Record W6921996753 · doi:10.11575/prism/28216

The Influence of Previous Injury History on Health and Fitness Outcomes in Junior High School Students

2016· other· en· W6921996753 on OpenAlexaboutno aff

Bibliographic record

VenuePRISM (University of Calgary) · 2016
Typeother
Languageen
FieldSocial Sciences
TopicGerman Social Sciences and History
Canadian institutionsnot available
Fundersnot available
KeywordsCardiorespiratory fitnessWaistBalance (ability)Balance testBody mass indexDynamic balanceInjury preventionFoot (prosody)Poison control

Abstract

fetched live from OpenAlex

Objective: To assess the influence of previous sport and recreational injury on body composition, cardiorespiratory fitness, musculoskeletal strength, and dynamic balance in junior high school students (ages 11-16). Methods: Cross-sectional study design. Participants included 1,039 students from six junior high schools in Calgary, Alberta, Canada. Demographics, injury history, and sport participation over the previous 12 months were collected using a questionnaire. Outcome measures included body mass index (BMI), waist circumference, predicted VO2max, vertical jump, eyes closed dynamic balance, and star excursion balance test reach distances. Results: Previously injured participants had significantly higher BMI and left foot balance times compared to uninjured participants. Exploratory analysis results suggest that those who sustained two injuries had a significantly increased BMI and waist circumference compared to those who sustained no injuries. Conclusions: Participants with a history of injury demonstrated more unhealthy body composition, but better left foot dynamic balance compared to uninjured participants.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score0.204

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.010
GPT teacher head0.263
Teacher spread0.253 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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