Does Using Multiple Computer Monitors for Office Tasks Affect User Experience?
Bibliographic record
Abstract
OBJECTIVE: To compare the impact of multiple computer monitor configurations on health and performance outcomes compared to the use of a single monitor. BACKGROUND: Multiple monitor configurations are used in office settings to promote increased productivity by providing more screen space; however, it is unknown if there are health-related trade-offs to increased productivity. METHOD: A systematic review was conducted according to the PRISMA statement guidelines and adapted the best evidence synthesis. RESULTS: Eighteen studies were included in our review. There was strong evidence that implementing dual monitors is in line with users' preference. There was also moderate evidence for controlled laboratory studies demonstrating that multiple monitors may increase task efficiency with decreased desktop interaction; however, implementing multiple monitors may also result in nonneutral neck postures for users. CONCLUSION: More research needs to be conducted on biomechanical exposures when using larger displays. Longitudinal field studies should be conducted to determine the influence of monitor interventions on health, productivity, and well-being. All studies must consider task complexity and user positioning and should measure health and productivity outcomes together. Researchers must also consider up-to-date purchasing trends when choosing the monitor configurations and sizes for their studies. APPLICATION: Regulatory bodies and practitioners can use the results to develop evidence-based monitor guidelines and inform decision-making in practice, respectively. Researchers can use this information to design future studies on monitor configurations that incorporate current purchasing trends.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".