The Influence of Exposure to Nature on Inpatient Hospital Stays: A Scoping Review
Bibliographic record
Abstract
AIM: To summarize the existing literature surrounding the influence of natural elements on course in hospital and to introduce clinicians to the concept of biophilic design and the potential for incorporation of nature into the hospital environment as a component of a therapeutic hospitalization. BACKGROUND: For decades, architects and designers have espoused the benefits of incorporating natural elements into the healthcare environment for therapeutic purposes. The benefits of this "biophilic" design philosophy has been investigated predominantly in long-term care or rehabilitation settings; however, some of the most appealing opportunities lie in the acute care setting. METHODS: This scoping review surveyed the literature surrounding the influence of exposure to nature on course in acute hospitalizations. After screening 12,979 citations, 41 articles were included. Exposures were divided into seven categories, the most common of which were the presence of a window/natural light, a natural scene through a window, and nature soundscapes. These articles were reviewed in a narrative fashion and thematic analysis was conducted. RESULTS: Studies were extremely heterogeneous in their design, research questions, and reported outcomes. Types of exposure to nature studied were exposure to a real natural scene through a window, presence of a window/nature light, nature in the healthcare environment, art depicting nature, direct contact with nature, nature soundscapes, and nature experienced through virtual reality (VR). CONCLUSIONS: Exposure to nature during an acute hospital admission appears to have a real but small therapeutic effect, predominantly on psychological metrics like anxiety/depression, pain, and patient satisfaction. Greater beneficial effects are seen with greater durations of exposure to nature and greater degrees of immersion into nature (e.g., creating multisensory experiences using emerging technology like VR).
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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 | 0.006 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".