Curricular nature-based learning in higher education to support mental and environmental health
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The relationship between human health and nature is increasingly recognized in diverse health science and environmental disciplines, demonstrating the fundamental interdisciplinary connection between humans and the natural environments we live in. Human-nature connectedness and a positive human-nature relationship have positive effects on mental health and well-being, and environmental benefits in the form of proenvironmental attitudes and behaviours, including environmental stewardship. However, nature deterioration associated with the climate crisis can directly and indirectly negatively impact human health, including mental health. The complex interconnections between mental health and nature in the context of the climate crisis, require a broad interdisciplinary perspective to understand the diverse elements contributing to and stemming from the global climate crisis. Yet, it is unrealistic for an individual person or even a community to address the entirety of the problem. Instead, individuals and communities should focus on implementing meaningful changes on a smaller local scale, which can be adapted and expanded for systemic implementation. One potential strategy is through education. There is strong evidence to support the mental health and environmental benefits of outdoor education, nature-based learning, and nature-based experiences, but these models focus on restricted age groups and may have considerable barriers to access. In this paper, we offer suggestions to empower individuals to make meaningful positive changes in their local environments for their own mental health, with the hope it will act as a path towards systemic change through embedding a model of curricular nature-based learning into education systems, including higher education.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it