Clinical Tools to Facilitate Workplace Accommodation After Treatment for an Upper Extremity Disorder
Bibliographic record
Abstract
Failure to implement work site accommodations for work-related upper extremity disorders (WRUEDs) may be a factor contributing to delayed functional recovery and relapse. The present study describes the use of the 38-item Job Requirements and Physical Demands (JRPD) scale, a self-report measure of ergonomic exposure, and other case management tools to improve accommodation efforts for 101 workers (75 women, 26 men) returning to work after lost time related to a WRUED. Items were categorized into five subscales based on item content: administrative, computer-related, workstation design, environmental, and equipment. Administrative risk factors were elevated among office clerks, whereas postal clerks and letter carriers reported more workstation design risk factors, and letter carriers and electrical/mechanical workers cited more equipment-related risk factors (p < 0.05). All occupational categories rated computer-related risk factors highest. The Integrated Case Management (ICM) approach, which relies on the JRPD scale to guide recommendations, was used with a subgroup of these workers (n = 53), resulting in 1.4 times more workplace accommodations per worker than with a non-ICM approach. Clinical use of the self-reported exposure measure within the overall workplace accommodation process may have been a factor contributing to more frequent accommodation in the ICM group. This study of a subgroup of workers' compensation cases highlights the need for additional investigation of tools to integrate ergonomic approaches within the workplace accommodation process.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".