Implementing Home Office Work at a Large Psychiatric University Hospital in Switzerland During the COVID-19 Pandemic: Field Report
Bibliographic record
Abstract
Background During the COVID-19 pandemic in 2020, psychiatric hospitals all over the world had to adapt their services to the prevailing governmental regulations. As a consequence, home office use and telepsychiatry boomed. Objective The purpose of this study was to evaluate the potential of home office use, its adoption, and the association of home office use with employees’ mental health in a large psychiatric university hospital in Switzerland. Methods We obtained and analyzed home office implementation and use data from the psychiatric university hospital’s information technology services. We also conducted a cross-sectional web-based survey to assess the employees’ attitudes toward the clinic’s crisis management during the COVID-19 pandemic in early 2020. Part of this web-based survey consisted of questions about home office use between March and June 2020, attitudes toward home office implementation, and mental health. Three mental health measures assessed depressive symptoms (Patient Health Questionnaire [PHQ]–2), anxiety (General Anxiety Disorder [GAD]–2), and stress factors (stress module of the PHQ-D); a cut-off score ≥3 was used for the PHQ-2 and GAD-2. Results Of the 200 participating employees, 69 reported that they had worked from home at least partially (34.5%). Home office use differed significantly across professional groups (χ162=72.72, P≤.001, n=200). Employees experienced neither depressive symptoms (mean 0.76, SD 1.14) nor anxiety (mean 0.70, SD 1.03). The employees reported minor psychosocial stressors (mean 2.83, SD 2.92). The number of reported stress factors varied significantly across groups with different levels of home office use (χ42=9.72, P=.04). Conclusions In general, home office implementation appears to be feasible for large psychiatric hospitals, however, it is not equally feasible for all professional groups. Professional groups that require personal contact with patients and technical or manual tasks must work onsite. Further evaluation of home office use in psychiatric hospitals up to the development of clinics that function merely online will follow in future research. The situation created by the COVID-19 pandemic served as a stepping stone to promote home office use and should be used to improve employees’ work–life balance, to save employers costs and foster other benefits.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".