Environmental Attributes for Healthcare Professional’s Well-Being
Bibliographic record
Abstract
Abstract The COVID-19 pandemic has been stressful for everyone and even more so for hard-working healthcare professionals. Contemporary hospitals are now endowed with environmental attributes that contribute to achieving well-being within their environment. However, these attributes tend to be focused on the patient and their experience. This paper examines these issues and describes the attributes of the physical environment that support healthcare professional’s well-being. Within a constructivist approach, the study was conducted in two care units in a mega hospital in Canada, before the arrival of the COVID-19 pandemic. Data collection includes a spatial evaluation of these care units, healthcare professionals’ spatial behavior, and 44 semi-structured interviews with various healthcare professionals, completed by the mental images. Thematic analysis and triangulation of the data set were conducted. Key attributes identified as promoting healthcare professionals’ well-being include light-color in care units, corridors and public areas of the hospital, and the cleanliness and art elements. Furthermore, panoramic views from the staff lounge, corridors, or elevator lobbies provide access to daylighting. This study highlights the importance of providing healthcare professionals break areas that allow them to find respite, particularly during periods of extreme stress such as COVID-19 pandemic.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".