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Record W2990711364 · doi:10.3390/ijerph16234667

The Association Between Fitness Test Scores and Musculoskeletal Injury in Police Officers

2019· article· en· W2990711364 on OpenAlexafffund
Liana Lentz, Jason R. Randall, Christine Guptill, Douglas P. Gross, Ambikaipakan Senthilselvan, Donald C. Voaklander

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2019
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsUniversity of Alberta
FundersGovernment of Alberta
KeywordsMusculoskeletal injuryTest (biology)Association (psychology)OfficerInjury preventionOccupational safety and healthHuman factors and ergonomicsMultivariate statisticsPhysical fitnessMedicinePoison controlMultivariate analysisPhysical therapyPsychologyEnvironmental healthInternal medicineStatisticsAlternative medicine

Abstract

fetched live from OpenAlex

A police officer’s career is hazardous and physically demanding. In order to perform occupational tasks effectively and without injury, officers require adequate physical abilities. The aim of this study was to investigate the relationship between scores on several fitness tests and musculoskeletal injury in a group of municipal police officers. This retrospective study used existing data to examine the relationship between risk of injury and fitness test performance. Injured and uninjured police officers scored significantly differently on several fitness measures. A multivariate regression indicated that a combination of age, sex, number of pull ups completed and maximal oxygen consumption (VO2max) best explained injury risk. Additionally, the findings indicated an interaction between sex and VO2max, and so the effect of VO2max on injury risk cannot be understood without accounting for sex.

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)0.000
metaresearch head score (Gemma)0.003
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.066
GPT teacher head0.478
Teacher spread0.413 · 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

Citations37
Published2019
Admission routes2
Has abstractyes

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