The role of at home workstation ergonomics and gender on musculoskeletal pain
Bibliographic record
Abstract
BACKGROUND: The recent mandate for university faculty and staff to work-from-home (WFH) during the COVID-19 pandemic has forced employees to work with sub-optimal ergonomic workstations that may change their musculoskeletal discomfort and pain. As women report more work-related musculoskeletal discomfort (WMSD), this effect may be exacerbated in women. OBJECTIVE: The purpose of this study was to describe university employee at-home office workstations, and explore if at-home workstation design mediates the effect of gender on musculoskeletal pain. METHODS: University employees completed a survey that focused on the WFH environment, at home workstation design and musculoskeletal pain. Descriptive statistics and regression analysis were used to analyze the responses. RESULTS: 61% of respondents reported an increase in musculoskeletal pain, with the neck, shoulders and lower back being reported most frequently. Women reported significantly greater musculoskeletal pain, but this relationship was significantly mediated by poor ergonomic design of the home workstation. Improper seat-height and monitor distance were statistically associated with total-body WMSD. CONCLUSIONS: WFH has worsened employee musculoskeletal health and the ergonomic gap between women and men in the workspace has persisted in the WFH environment, with seat height and monitor distance being identified as significant predictors of discomfort/pain.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".