Accessing Green Spaces Within a Healthcare Setting: A Mixed Studies Review of Barriers and Facilitators
Bibliographic record
Abstract
This review describes the facilitators and barriers impacting on passive access to green spaces within healthcare settings. A systematic mixed-studies review was undertaken to review the quantitative and qualitative evidence on access to green spaces within healthcare settings, as well as to review the methodological quality of the studies eligible for inclusion. A total of 24 articles met the inclusion criteria and were included in the review. The barriers to access were grouped into three themes: "awareness," "accessibility," and "comfort." The facilitators were grouped into 13 themes: "flora and foliage," "views," "water features," "sun, rain, fresh air, wind," "animal life," "diverse textures, heights, shapes," "lawn," "natural versus artificial material," "rest areas," "shade," "private areas," "play equipment," and "safety." These findings can be explained through multiple lenses, using existing theories on contact with nature and supportive garden design. In an era of elevated stress, patient admissions, and staff turnover in hospitals, and rising costs of providing healthcare services, the creation of settings conducive to health promotion, stress reduction, and faster recovery is relevant and timely. This article, which has collated over three decades of research evidence, is invaluable in addressing this issue.
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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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".