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Record W1994361041 · doi:10.1108/17538350810865587

The health and safety of young people at work: a Canadian perspective

2008· article· en· W1994361041 on OpenAlexaffabout
E. V. McCloskey

Bibliographic record

VenueInternational Journal of Workplace Health Management · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Farm Safety
Canadian institutionsWorkers Compensation Board of British Columbia
Fundersnot available
KeywordsOriginalityGovernment (linguistics)Public relationsWork (physics)Social marketingValue (mathematics)Public healthOccupational safety and healthPerspective (graphical)PsychologyMedicineNursingPolitical scienceSocial psychologyEngineering

Abstract

fetched live from OpenAlex

Purpose To review recent research on work injuries among young workers, to examine efforts of Canadian authorities to reduce injury rates among this group, and to identify indicators of success. The information is useful in the design of public health programs or within organizations employing young workers, and identifies future research needs. Design/methodology/approach A range of recently published (1998‐2007) systematic and narrative reviews, research papers, government documents and websites, primarily from Canadian sources, was reviewed. Documents were critically reviewed to identify factors associated with increased risk of injury, and to examine the use of social marketing approaches in the prevention of injury. Findings Recent research tends to confirm that the types of jobs that young workers do, and the fact that they often have only short‐term experience in the job, are major factors contributing to the increased risk that young workers experience. Social marketing is being widely used in Canada as a prevention approach, but research on its effectiveness is in its infancy. Originality/value The paper summarises recent research on young worker safety, highlighting findings that are of value for program design and future research needs.

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)0.000
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 score0.399
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.013
GPT teacher head0.250
Teacher spread0.237 · 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

Citations12
Published2008
Admission routes2
Has abstractyes

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