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Record W2181240450 · doi:10.3233/wor-152129

Expanding vocational retraining options for injured workers: An experiment in worker choice

2015· article· en· W2181240450 on OpenAlexfundno aff
Jeanne M. Sears, Thomas M. Wickizer, Beryl A. Schulman

Bibliographic record

VenueWork · 2015
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
FundersWashington State Department of Labor and IndustriesWorkplace Safety and Insurance Board
KeywordsRetrainingWageVocational educationBusinessWorkers' compensationLabour economicsCompensation (psychology)Medical educationMedicinePsychologyEconomicsPedagogySocial psychology

Abstract

fetched live from OpenAlex

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.

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 score0.204
Threshold uncertainty score0.327

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 head0.068
GPT teacher head0.383
Teacher spread0.315 · 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

Citations5
Published2015
Admission routes1
Has abstractyes

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