Bibliographic record
Abstract
People spend much of their waking time in their workplaces (approximately 33% on a weekly basis), which raises the possibility that the conditions they experience at work influence their health and well-being. The workplace design literature has given scant attention to mental health outcomes, instead focusing on healthy populations. Conversely, the mental health literature gives scant attention to the potential contribution of workplace design in preventing mental health problems; nor does it provide much insight into facilitating return to work. Taken together, however, the literature does suggest both lines of research and possible interventions. Existing knowledge proposes that workplace design can influence mental health via the effects of light exposure on circadian regulation, social behaviour and affect; the effects of aesthetic judgement on at-work mood and physical well-being and at-home sleep quality; access to nature and recovery from stressful experiences; and privacy regulation and stimulus control. This paper includes a short review of the literature in this area, proposals for new research directions and consideration of the implications of this information on the design choices made by business owners, designers and facility managers. Providing suitable working conditions for all employees avoids stigmatizing employees who have mental health problems, while facilitating prevention and return to work among those who do.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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; both teacher heads agree on what is shown here.
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".