Hospital Outdoor Spaces as Respite Areas for Healthcare Staff During the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic has created considerable implications for healthcare staff around the globe. During the pandemic, the frontline healthcare workers experience intense anxiety, stress, burnout, and psychological breakdown, with severe implications on their mental and physical well-being. In addition to these implications, anxiety and stress can hinder their productivity and ability to perform their duties efficiently. The literature indicates that hospital gardens and contact with nature can help alleviate psychological distress among hospital staff. However, few studies investigated the role of outdoor spaces as areas for respite and work breaks in healthcare facilities during the pandemic. The present opinion paper highlights the challenges of job stress and psychological distress health workers face during the pandemic. This article also underscores the role of hospital outdoor spaces and garden facilities in coping with the challenges. While other measures to reduce stress among hospital staff and ensure their health and safety are important, hospital administrators and relevant government agencies should also emphasize the provision of gardens and open spaces in healthcare facilities. These spaces can act as potential areas for respite for hospital staff to help them cope with the stress and anxiety accumulated through working under crises.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".