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Record W2161047199 · doi:10.1080/1059924x.2015.1042614

Farm Activities and Agricultural Injuries in Youth and Young Adult Workers

2015· article· en· W2161047199 on OpenAlexafffundabout
Yvonne DeWit, William Pickett, Joshua Lawson, James A. Dosman, for the Saskatchewan Farm Injury Co

Bibliographic record

VenueJournal of Agromedicine · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Farm Safety
Canadian institutionsUniversity of SaskatchewanQueen's University
FundersCanadian Institutes of Health Research
KeywordsOccupational safety and healthAgricultureYoung adultFarm workersInjury preventionEnvironmental healthMedicineHerdPoison controlHuman factors and ergonomicsCohort studyConfidence intervalRelative riskSuicide preventionDemographyGerontologyVeterinary medicineGeography

Abstract

fetched live from OpenAlex

Youth and young adults who work in the agricultural sector experience high rates of injury. This study aimed to investigate relations between high-risk farm activities and the occurrence of agricultural injuries in these vulnerable groups. A cross-sectional analysis was conducted using written questionnaire data from 1135 youth and young adults from the Saskatchewan Farm Injury Cohort. The prevalence of agricultural injury was estimated at 4.9%/year (95% confidence interval [CI]: 3.7, 6.2). After adjustment for important covariates, duration of farm work was strongly associated with the occurrence of injury (risk ratio [RR] = 8.0 [95% CI: 1.7, 36.7] for 10-34 vs. <10 hours/week; RR = 10.3 [95% CI: 2.2, 47.5] for those working 35+ hours/week). Tractor maintenance, tractor operation, chores with large animals, herd maintenance activities, and veterinary activities were identified as risk factors for agricultural injury. Risks for agricultural injury among youth and young adults on farms relate directly to the amounts and types of farm work exposures that young people engage in.

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)0.001
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 score0.033
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.001
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 head0.014
GPT teacher head0.213
Teacher spread0.198 · 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

Citations21
Published2015
Admission routes3
Has abstractyes

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