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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".