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Record W3034127658 · doi:10.1080/10803548.2020.1763609

Do older workers suffer more workplace injuries? A systematic review

2020· review· en· W3034127658 on OpenAlexaff
Gonzalo Bravo, Carlos Viviani, Martin Lavallière, Pedro Arezes, Marta Martínez, Imán Dianat, Sara Bragança, Héctor Ignacio Castellucci

Bibliographic record

VenueInternational Journal of Occupational Safety and Ergonomics · 2020
Typereview
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsMedicineOccupational safety and healthInjury preventionPoison controlHuman factors and ergonomicsSuicide preventionGerontologyDemographyEnvironmental health

Abstract

fetched live from OpenAlex

Aging populations are a dramatically increased worldwide trend, both in developed and developing countries. This study examines the prevalence of fatal and non-fatal work-related injuries between young (<45 years old) and older (≥45 years old) workers. A systematic literature review aimed at examining studies comparing safety outcomes, namely fatal and non-fatal injuries, between older and younger workers. Results show that 50% of the reviewed papers suggest that fatal injuries are suffered mainly by older workers, while the remaining 50% show no differences between older and younger workers. Regarding non-fatal injuries, 49% of the reviewed papers found no relationship between workers' age; 31% found increased age as a protective factor against non-fatal injuries; and 19% showed that older workers had a higher risk of non-fatal injuries than younger ones. This review suggests that older workers experience higher rates of fatal injuries, and younger workers experience higher rates of non-fatal injuries.

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)0.003
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.005
Bibliometrics0.0090.009
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.094
GPT teacher head0.499
Teacher spread0.404 · 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 designSystematic review
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

Citations48
Published2020
Admission routes1
Has abstractyes

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