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Record W2163679481 · doi:10.1093/heapro/dar099

Is this the new smoking? An expert panel review of the York University OHV health benefits study

2012· review· en· W2163679481 on OpenAlexaffabout
Glyn Bissix, Keith MacCormick, C. H. Milburn

Bibliographic record

VenueHealth Promotion International · 2012
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Farm Safety
Canadian institutionsCape Breton Regional HospitalAcadia University
Fundersnot available
KeywordsRecreationApplied psychologyConceptualizationPsychologyPoison controlOccupational safety and healthMedical educationMedicinePublic relationsEnvironmental healthPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Recently, a study by Burr and his associates culminating in three peer reviewed journal articles and a string of press releases emanating from the off-highway vehicle (OHV) industry claimed that increasing riding time on all terrain recreational vehicles (ATVs) and off road motorcycles (ORMs) could meet the basic guidelines of the American College of Sports Medicine and Health Canada for sufficient physical activity leading to positive health adaptations. Should this be true, this study could revolutionize the way that health professionals prescribe physical activity. To examine the efficacy of these claims, the authors convened an expert panel to examine these publications to particularly focus on the problem conceptualization, the research methodology including sample selection and controls, the presentation and interpretation of results and the veracity of their conclusions. The experts concluded, while never questioning the laboratory and field measurements, that there were a number of conceptual, methodological and interpretive limitations and errors that rendered the claims of health benefits indefensible. Furthermore, the researchers largely failed to take account of the healthcare costs associated with riding OHVs which according to the epidemiology literature, and particularly for ATVs, are considerable.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.974
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.230
GPT teacher head0.367
Teacher spread0.137 · 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.

Study designNot applicable
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

Citations5
Published2012
Admission routes2
Has abstractyes

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