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Record W2941747906 · doi:10.1177/1077558719845726

Coverage Gaps and Cost-Shifting for Work-Related Injury and Illness: Who Bears the Financial Burden?

2019· review· en· W2941747906 on OpenAlexaff
Jeanne M. Sears, Amy T. Edmonds, Norma B. Coe

Bibliographic record

VenueMedical Care Research and Review · 2019
Typereview
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsInstitute for Work & Health
FundersNational Institute for Occupational Safety and Health
KeywordsWork (physics)Occupational safety and healthWorkers' compensationCompensation (psychology)BusinessHealth careEconomic costMedicineEnvironmental healthActuarial sciencePsychologyEconomic growthEconomics

Abstract

fetched live from OpenAlex

The heavy economic burden of work-related injury/illness falls not only on employers and workers' compensation systems, but increasingly on health care systems, health and disability insurance, social safety net programs, and workers and their families. We present a flow diagram illustrating mechanisms responsible for the financial burden of occupational injury/illness borne by social safety net programs and by workers and their families, due to cost-shifting and gaps in workers' compensation coverage. This flow diagram depicts various pathways leading to coverage gaps that may shift the burden of occupational injury/illness-related health care and disability costs ultimately to workers, particularly the most socioeconomically vulnerable. We describe existing research and important research gaps linked to specific pathways in the flow diagram. This flow diagram was developed to facilitate more detailed and comprehensive research into the financial burden imposed by work-related injury/illness, in order to focus policy efforts where improvement is most needed.

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)0.014
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.790
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.005
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 head0.202
GPT teacher head0.577
Teacher spread0.374 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations19
Published2019
Admission routes1
Has abstractyes

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