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Education-to-job mismatch and the risk of work injury

2012· article· en· W2117122494 on OpenAlexaffabout
Stéphanie Premji, Peter Smith

Bibliographic record

VenueInjury Prevention · 2012
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsInstitute for Work & HealthPublic Health OntarioUniversity of TorontoMcMaster University
Fundersnot available
KeywordsForensic engineeringOccupational safety and healthWork (physics)Injury preventionPoison controlHuman factors and ergonomicsSuicide preventionEngineeringMedical emergencyPsychologyMedicineMechanical engineering

Abstract

fetched live from OpenAlex

OBJECTIVES: To examine the association between education-to-job mismatch and work injury. METHODS: Cross-sectional data from the 2003 and 2005 Canadian Community Health Surveys (n=63,462) were used to examine the relationship between having an educational level that is incongruent with occupational skills requirements and the risk of sustaining a work injury requiring medical attention or a work-related repetitive movement injury (RMI). The effect on injury of the interaction of overeducation with recent immigrant status was also examined. Models were stratified by sex and adjusted for possible confounders. Occupational physical demands were conceptualised as a potential mediating variable. RESULTS: After adjustment for covariates, over-education was associated with work injury and RMI for both sexes. Adjustment for occupational demands attenuated the impact on work injury but did not eliminate the effect on RMI among men. The interaction of over-education and recent immigrant status resulted among men in a fourfold increase in the odds of work injury compared with non-recent immigrants who were not over-educated. After adjustment for occupational demands, over-educated recent immigrant men still had more than a twofold increase in the odds of injury. CONCLUSIONS: The risk of sustaining a work injury is higher among those whose education exceeds that of job requirements. These findings highlight the need to address barriers to suitable employment, particularly among recent immigrants.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.514
Threshold uncertainty score0.187

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 head0.007
GPT teacher head0.307
Teacher spread0.300 · 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

Citations44
Published2012
Admission routes2
Has abstractyes

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