Harnessing the power of nature in surgery: A systematic scoping review of nature prescribing and measures of benefit
Bibliographic record
Abstract
BACKGROUND: Natural environments can reduce stress and benefit overall health and nature prescriptions may be a useful adjunct for postoperative care in surgical patients. We sought to identify characteristics and health measures of nature prescriptions in surgery. METHODS: We undertook a scoping review of 5 bibliometric databases (to February 2024) and citations of peer-reviewed original studies examining nature prescription interventions in patients undergoing major surgical procedures requiring anesthesia and hospital admission. Standardized data extraction and narrative synthesis were performed. RESULTS: The search found 2,175 deduplicated eligible records and 20 full-texts for screening. Ten studies were included and covered auditory (n = 3), visual (n = 3), experiential (n = 3), or mixed interventions (n = 1), with small sample sizes (median, 106; interquartile range, 252). About 50% focused on cardiothoracic surgical patients. Interventions varied from viewing plants/trees, listening to bird/water/forest sounds or being physically immersed. Nearly all included studies reported patient-reported outcome measures (eg, anxiety, pain or mood) that significantly improved across all intervention types. Cardiovascular (eg, heart rate, blood pressure) and respiratory (eg, respiratory rate, vital capacity) outcomes were frequently measured, with greater improvements observed in the nature-based intervention groups compared with controls. Few included studies assess other clinical measures (eg, mortality, length of stay, complications) and only 2 evaluated medications to show reductions in the amount of analgesic consumption after being exposed to nature interventions. CONCLUSION: Nature prescribing in surgery appears diverse and shows promise for improving postoperative recovery and well-being through reduced anxiety, pain, cardiorespiratory stress, medication use, and improved mood. Medical and research attention to include nature-based therapy in surgery seems warranted.
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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 | 0.035 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".