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Record W2100959645 · doi:10.5271/sjweh.1126

Work disability absence among young workers with respect to earnings losses in the following year

2007· article· en· W2100959645 on OpenAlexaff
F. Curtis Breslin, Emile Tompa, Ryan Zhao, Benjamin C. Amick, Jason D. Pole, Peter Smith, Sheilah Hogg‐Johnson

Bibliographic record

VenueScandinavian Journal of Work Environment & Health · 2007
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsMcMaster UniversityUniversity of TorontoInstitute for Work & Health
FundersNational Institute for Occupational Safety and Health
KeywordsEarningsYoung adultWork (physics)Disability benefitsPsychologyMedicineDemographyGerontologyDemographic economicsEconomicsSocial securityFinance

Abstract

fetched live from OpenAlex

OBJECTIVES: The primary objective of this study was to evaluate the earnings losses that young workers experience in the year after a work disability absence. METHODS: The sample consisted of workers aged 16 to 24 years from a longitudinal survey of a representative sample of Canadians. Young workers who lost > or =5 days of work due to work disability or illness (ie, work disability absence) were matched to uninjured controls on the basis of age, gender, preabsence earnings, and student status. This matching procedure resulted in 173 cases and 795 controls. The outcome measure was the difference in earnings the year after the work disability episode between injured cases and their uninjured controls. RESULTS: An analysis of variance indicated that young workers experiencing a work disability absence had significantly fewer earnings than their controls in the year after the absence (P<0.05). This earnings loss was not due to between-group differences in school activity or workhours in the year after the work absence. CONCLUSIONS: No study to date has estimated the impact of work-related disability on earnings trajectories among young workers. The findings of the present study indicate that earnings losses can occur among young workers even during their transition into the labor market. Documenting the economic impacts of work injuries early in one's worklife can provide information for policy debates on the allocation of resources to control workplace hazards where teenagers and young adults work and debates on the determination of fair and adequate benefits for young 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)0.013
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 score0.013
Threshold uncertainty score0.838

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.036
GPT teacher head0.389
Teacher spread0.353 · 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

Citations33
Published2007
Admission routes1
Has abstractyes

Explore more

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