Expanding vocational retraining options for injured workers: An experiment in worker choice
Bibliographic record
Abstract
BACKGROUND: An innovative self-directed vocational retraining alternative (Option 2) has been offered to eligible Washington State injured workers since 2008. OBJECTIVE: We aimed to describe: (1) how frequently Option 2 was selected and by whom, (2) the extent to which Option 2 workers used their reserved retraining funds, and (3) how worker satisfaction and employment outcomes for Option 2 workers compared with those of workers undergoing traditional vocational retraining. METHODS: Five-year cohort study involving workers' compensation data, state wage files, and two worker surveys. RESULTS: Fewer than 25% of Option 2 workers used their retraining funds. Retraining fund use was associated with better employment outcomes. Workers who were older, whose preferred language was not English, or who had lower pre-injury wages or less education, were least likely to use Option 2 retraining funds. Many workers chose Option 2 because they thought the approved traditional retraining plan was not a good fit for them. CONCLUSIONS: Self-directed retraining may benefit workers who have the ability, resources, and motivation to independently identify and complete retraining. Additional efforts may be needed to ensure that traditional retraining plans are well-suited to workers' circumstances, and to identify and remove barriers to use of reserved retraining funds.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".