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Record W1480294081 · doi:10.36834/cmej.36609

Family medicine residents' risk of adverse motor vehicle events: comparison between rural and urban placements.

2013· article· en· W1480294081 on OpenAlexaffvenueabout
Fred Janke, Bonnie Dobbs, Rhianne McKay, Meghan Lindsell, Оксана Бабенко

Bibliographic record

VenueCanadian Medical Education Journal · 2013
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAdverse effectMedicineEnvironmental healthPhysical medicine and rehabilitationTraditional medicineFamily medicineInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Sleep deprivation and fatigue are associated with long and irregular work hours. These work patterns are common to medical residents. Motor vehicle crashes (MVCs) are a leading cause of injury related deaths in Canada, with MVC fatality rates in rural areas up to three times higher than in urban areas. OBJECTIVES: To: 1) examine the number of adverse motor vehicle events (AMVEs) in family medicine residents in Canada; 2) assess whether residents with rural placements are at greater risk of experiencing AMVEs than urban residents; and 3) determine if family medicine residency programs across Canada have travel policies in place. METHODOLOGY: A prospective, cross-sectional study, using a national survey of second-year family medicine residents. RESULTS: A higher percentage of rural residents reported AMVEs than urban residents. The trend was for rural residents to be involved in more MVCs during residency, while urban residents were more likely to be involved in close calls. The majority of Canadian medical schools do not have resident travel policies in place. CONCLUSION: AMVEs are common in family medicine residents, with a trend for the number of MVCs to be greater for rural residents. These data support the need for development and incorporation of travel policies by medical schools.

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)0.007
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.253
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0260.000

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 head0.029
GPT teacher head0.410
Teacher spread0.381 · 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 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

Citations8
Published2013
Admission routes3
Has abstractyes

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