How salutogenic workplace characteristics influence psychological and cognitive responses in a virtual environment
Bibliographic record
Abstract
Until today, most research focussed on the effects of pathogenic workplace demands on employee illness instead of on salutogenic resources on health. Using a stated-choice experiment in a virtual open-plan office, this study identifies key design aspects that enhance psychological and cognitive responses, ultimately improving health outcomes. The study systematically varied six workplace attributes: screens between workstations, occupancy rate, presence of plants, views outside, window-to-wall ratio (WWR), and colour palette. Each attribute predicted perceptions of at least one psychological or cognitive state. Plants had the highest relative importance for all expected responses but views outside with ample daylight, red/warm wall colours, and a low occupancy rate without screens between desks were also important. Low-cost interventions like adding plants, removing screens, and using warm wall colours can contribute to a healthier open-plan office environment. These insights can guide workplace managers to design environments that support employees’ mental states and health.Practitioner summary: Salutogenic workplace resources that promote health have been understudied. This study aimed to show which workplace characteristics caused positive psychological and cognitive responses to improve health, using a stated-choice experiment in a virtual office environment. Plants in the office were the most important attribute for employees’ psychological and cognitive responses.
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".