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Record W2190621308 · doi:10.3233/wor-2003-00328

The costs of job accommodations for employees with low back pain

2003· article· en· W2190621308 on OpenAlexaboutno aff
Manny Halpern

Bibliographic record

VenueWork · 2003
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsDocumentationPsychological interventionCompensation (psychology)Intervention (counseling)Process (computing)Workers' compensationOperations managementHazardQuarter (Canadian coin)Low back painBusinessMedicinePsychologyComputer scienceNursingEconomics

Abstract

fetched live from OpenAlex

Accommodations are interventions designed to reduce exposure to factors that limit the activities of an impaired individual. The process incurs costs due to job analysis, implementation and follow up. This theoretical paper expands a model of the ergonomic intervention process and provides data on costs of accommodating individuals with musculoskeletal disorders, particularly low back pain. Accommodations begin with evaluation and documentation of exposure to risk factors. The methods depend on the budget and clinical utility of the data. A full hazard analysis may require 1-hour managerial time plus 1-hour employee time per job. Studies by the Department of Labor and others indicate that at least a quarter of problem jobs could be addressed faster and for less than US dollars 500. Follow-up incurs variable employee and managerial time; an ergonomist may be required in 15% of cases. The benefit is expected mainly from reducing compensation costs. Universal solutions could increase the benefits.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.010
GPT teacher head0.268
Teacher spread0.258 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2003
Admission routes1
Has abstractyes

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