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Record W2884736282 · doi:10.1002/ajim.22872

Agricultural injuries among farm and non‐farm children and adolescents in Alberta, Canada

2018· article· en· W2884736282 on OpenAlexafffundabout
Kyungsu Kim, Jeremy Beach, Ambikaipakan Senthilselvan, Niko Yiannakoulias, Hyocher Kim, Donald C. Voaklander

Bibliographic record

VenueAmerican Journal of Industrial Medicine · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Farm Safety
Canadian institutionsSpinal Cord Injury AlbertaMcMaster UniversityAlberta HealthUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsMedicineAgricultureIncidence (geometry)Occupational safety and healthEnvironmental healthRural areaInjury preventionPoison controlDemographyGeography

Abstract

fetched live from OpenAlex

BACKGROUND: Understanding of the specific risk of agricultural injury sustained by different populations of children and adolescents is needed for effective safety intervention. OBJECTIVE: To compare the rates and patterns of agricultural injury incidence (fatal and non-fatal injury) between farm and non-farm children less than 18 years of age in Alberta, Canada. METHODS: A total of 115 378 children (five subgroups: two groups of farm children and three groups of non-farm children) in Alberta were followed from 1999 to 2010 to examine injury incidence using the linkage of three administrative health databases. A recurrent event survival analysis using Cox proportional hazards regression was carried out. RESULTS: A total of 1 849 agricultural injury episodes (1 616 emergency department visits, 225 hospitalizations, and 8 deaths) were identified from 1999 to 2010. The age- and gender-adjusted rate (per 100 000 person years) of agricultural injury was 672.3 for rural-living farm children, 369.4 for urban-living farm children, 180.2 for rural non-First Nations (FN) children, 64.4 for rural FN children, and 23.7 for urban children in descending order. CONCLUSION: Specific strategies for different children's populations to prevent agricultural injuries and to extend agricultural injury controls to non-farming populations are needed.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.248
Threshold uncertainty score0.369

Codex and Gemma teacher scores by category

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

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 head0.007
GPT teacher head0.200
Teacher spread0.193 · 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.

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

Citations14
Published2018
Admission routes3
Has abstractyes

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