The Conceptualization of the Natural Environment in Healthcare Facilities: A Scoping Review
Bibliographic record
Abstract
OBJECTIVES: To identify how the natural environment (NE) in healthcare has been conceptualized. BACKGROUND: The NE appears to afford significant therapeutic benefits. A clear concept of the NE in healthcare affords a shared understanding from which to advance science to facilitate comparisons across contexts. In this article, the various meanings of the NE were brought together into one framework by which to map its themes and their relationships. METHOD: A scoping review was conducted using database searches in MEDLINE, EMBASE, PsycINFO, CINAHL, and Cochrane for articles published up to July 2018. The bibliography of the included articles were manually searched for published books. RESULTS: This review includes 137 peer-reviewed articles and research-based books from 27 countries. A conceptual framework was developed to identify five themes that conceptualize the NE in healthcare: (1) definitions of the NE in healthcare, (2) processes of the NE in healthcare, (3) usages of the NE in healthcare, (4) opinions about the NE in healthcare, and (5) NE's impacts on health and work outcomes in healthcare. These themes are filtered by the NE's physical and programmatic designs; changes in one affect the others. Definitions of the NE are described as human-made space, located in the indoors and outdoors, containing elements of nature and designed with the purpose to positively influence humans. Processes are described as the participatory approach in NE's development and its therapeutic goals. Usages are categorized into nature contact, frequency of usage, and accessibility. Opinions are accounted for by perceptions, preference, and satisfaction. Outcomes are related to physical health, mental health and well-being and work. CONCLUSIONS: This framework contributes to the conceptual discussion and emphasizes NE's complementarity to the biomedical healthcare system.
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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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.019 | 0.024 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| 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".