Nature-based care opportunities and barriers in oncology contexts: a modified international e-Delphi survey
Bibliographic record
Abstract
OBJECTIVE: To develop recommendations regarding opportunities and barriers for nature-based care in oncology contexts using a structured knowledge generation process involving relevant healthcare and design experts. DESIGN: Four-round modified electronic Delphi study. Oncology patients' nature-based recommendations, uncovered in preceding qualitative investigation, were included in the first round for the expert participants' consideration. Key items (opportunities and barriers) were developed using data aggregation and synthesis, followed by item prioritisation and 10-point Likert scale ranking (1=not important, 10=very important). Descriptive statistics were calculated to assess items of highest importance representing expert recommendations. CONTEXT: Online Delphi process constituting an electronic international survey. PARTICIPANTS: A purposive sample of 200 potential panellists (recruitment target n=40) comprising healthcare practitioners, managers, designers, architects and researchers were invited to participate; experts were identified via research networks, snowballing and systematic literature review. RESULTS: 38 experts across seven countries (Australia, USA, UK, New Zealand, Canada, Denmark and Sweden) returned questionnaire 1, which determined consent and acceptance for participation. Initial response rate was 19%, and subsequent response rates were 84%, 82% and 84% for rounds 2, 3 and 4, respectively. The Delphi panel developed recommendations consisting of 10 opportunities and 10 barriers. The following opportunities were rated to be of highest importance: window views from clinical areas onto nature; outdoor settings, gardens and courtyards with easy and effortless access; and nature-based physical exercise adapted to patient requirements. Highest-rated barriers for nature-based oncology care included lack of knowledge and awareness about benefits of nature engagement and inaccessibility, not considering access requirements for the very sick and frail. CONCLUSIONS: Experts suggested and agreed on a set of recommendations, which represent critical considerations for the safe adoption of nature-based oncology opportunities. These findings fill a gap in understanding about helpful nature-based oncology care and may translate into oncology design and innovation.
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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 | 0.041 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".