The ergonomic assessment tool for arthritis: Development and pilot testing
Bibliographic record
Abstract
OBJECTIVE: Ergonomic assessment and recommendations may help people with arthritis maintain employment; however, most ergonomic tools are designed to assess injury risk in the general population and are not specific to the needs of people with inflammatory arthritis (IA). Our objectives were to design and pilot test an ergonomic assessment tool for people with IA and to propose ergonomic modifications to prevent work loss and maintain at-work productivity. METHODS: Relevant content was identified in a literature review by an interdisciplinary team. Respecting some clients' reluctance to disclose arthritis to employers, no work site visit was required. An initial assessment tool was reviewed by a 4-person expert panel, revised and pretested with 13 adults with IA by 3 occupational therapists (OTs). The final tool, comprised of a self-assessment, an interview guide, and a solutions summary, was used in a pilot test of a multifaceted program designed to prevent work loss and maintain at-work productivity. One OT conducted all ergonomic consultations and followed up with phone calls at 1 month. Implementation of recommendations was evaluated at 3, 6, and 12 months. RESULTS: Nineteen women (mean age 51 years) with IA (mean disease duration 12 years) completed ergonomic assessments. A range of risks were identified and 87 recommendations were made (mean 4.5 per participant). At 1 year, 85% of recommendations had been implemented by 74% of the participants. CONCLUSION: The Ergonomic Assessment Tool for Arthritis is a feasible and comprehensive process for identifying ergonomic job accommodations.
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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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".