Work disability absence among young workers with respect to earnings losses in the following year
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".