Challenges in nature-based health and therapy research and critical considerations for application in musculoskeletal health
Bibliographic record
Abstract
Nature-based health and therapy (NBHT) is a term incorporating a broad suite of practices that focus on engagement with the natural world and nature-rich spaces for potential physical and mental health benefits. As healthcare professions such as physiotherapy and osteopathy move away from biomedical/reductionist models of care for complex conditions towards approaches which take into account social and environmental determinants of health, NBHT may become part of clinical interventions and public health messaging. However, there are multiple challenges in aspects of NBHT research and application, from methodological issues in the primary research base, to questions of environmental injustice and access inequalities in many areas. In addition, engaging with natural environments which are vulnerable to the entwinned threats of climate change and biodiversity collapse requires consideration of the effects of ecological disturbance and the underlying anthropocentric/utilitarian view of the natural world. In this perspective, we outline a critique of NBHT literature and offer positive suggestions for how better-quality research can be conducted and implemented by focusing on local environmental, social, and political factors. We conclude by outlining a set of critical considerations that healthcare professionals might use to develop and implement NBHT programmes in their specific regional contexts.
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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 | 0.402 | 0.322 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.016 | 0.129 |
| Scholarly communication | 0.031 | 0.047 |
| Open science | 0.015 | 0.025 |
| Research integrity | 0.028 | 0.044 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".