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Record W2284609908 · doi:10.1080/17457300.2015.1132734

Injuries and their burden in insured construction workers in Iran, 2012

2016· article· en· W2284609908 on OpenAlexaff
Seyed Esmaeil Hatami, Narges Khanjani, Mohammad Alavinia, Mohammad Reza Ghotbi Ravandi

Bibliographic record

VenueInternational Journal of Injury Control and Safety Promotion · 2016
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsUniversity Health NetworkToronto Rehabilitation Institute
Fundersnot available
KeywordsYears of potential life lostBurden of diseaseMedicineFalling (accident)Poison controlOccupational safety and healthDisease burdenInjury preventionIncidence (geometry)Suicide preventionEnvironmental healthDemographyDisability-adjusted life yearHuman factors and ergonomicsGerontologyLife expectancyPopulation

Abstract

fetched live from OpenAlex

The present study used disability adjusted life years (DALY) to estimate the burden of external cause of injuries in construction workers insured in Iran in 2012. The Global Burden of Disease method (2010) was used to estimate the years of life lost due to death (YLL) and years of life lost due to disability (YLD). DALY was calculated as the sum of YLL and YLD. There were 5352 injured construction workers in Iran (11.25 individuals per 1000). Falling was the most common incidence and included 2490 individuals (46.53%). Totally, DALY was estimated 18,557 years for all age groups and both genders including 17,821 YLD (96%) and 736 YLL (4%). The DALY related to construction work is high in Iran and it has notably affected the young. Hence more preventive methods should be applied to reduce the overall burden of specific external cause of injuries especially in young and inexperienced workers.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.286
Threshold uncertainty score0.315

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.027
GPT teacher head0.383
Teacher spread0.356 · 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
Published2016
Admission routes1
Has abstractyes

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