Investigating the influence of keyboard inclinations on sitting and standing workstations
Bibliographic record
Abstract
Given the growing global computer workforce, concerns exist about the escalation of computer related injury with Carpal tunnel syndrome (CTS) being one of the most reported work-related musculoskeletal disorders (WMSDs) among office workers. The optimal range of keyboard angles for sitting and standing positions based on wrist posture, forearm muscle activities and user preference as well as the keyboard location in relation to user position were analysed. 30 volunteers with an above 40 words per minute typing speed participated in this study. Result show that, although user prefer to use positive keyboard angle, the negatively tilted keyboard is more ergonomically friendly at both sitting and standing workstations, reducing muscle activity and awkward wrist posture while maintaining performance. The findings indicate that negative sloped keyboard might have the possibility to reduce the risk of developing CTS in office workers.Practitioner summary: This study determines the range of optimal slope of keyboard angle in a sit and stand workstation. Our results indicate a trend in the negative slope keyboard as an ergonomically friendly option for the intervention to Carpal tunnel pressure.
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 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 | 0.000 | 0.004 |
| 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) | 0.004 | 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".