Ergonomic interventions for office workers with musculoskeletal disorders: A systematic review
Bibliographic record
Abstract
INTRODUCTION: Ergonomic interventions designed for office and computer work have become widely available and heavily marketed but there is little evidence to support their use with workers who already have a musculoskeletal disorder (MSD). The purpose of any ergonomic intervention can be to improve worker comfort, safety and/or productivity. The ergonomic research in secondary prevention typically focuses outcomes on improved worker comfort but less if any emphasis has been put on productivity and safety. The purpose of this study was to determine the level and quality of evidence supporting ergonomic interventions to improve the comfort, safety and/or productivity of office workers with symptoms of MSDs. METHOD: A search of the ergonomic intervention literature based on MSDs of four body areas (low back, upper limb, eye and neck) was employed. The studies underwent two levels of analysis for inclusion in a best-evidence synthesis approach, which included a priori evaluation of specific interventions relative to outcomes of comfort, safety and/or productivity. RESULTS: Twenty-seven out of 202 articles were synthesized based on relevance, quality and significant results. Only 8 articles were determined high quality and no strong levels of evidence were identified. Levels of evidence for specific ergonomic interventions ranged from insufficient to moderate. Generally outcomes were focused mostly on improved comfort of workers. CONCLUSIONS: There is still limited quality research that addresses ergonomic interventions designed for secondary prevention. Further high quality studies are needed to support evidence-based ergonomic interventions in practice. For all stakeholders to fully evaluate the usefulness of the ergonomic intervention studies need to attend to outcomes not only of worker comfort but also to productivity and safety.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".