Forest bathing: a narrative review of the effects on health for outdoor and environmental education use in Canada
Bibliographic record
Abstract
Background: Education and health professionals from a range of disciplines seek alternatives to promote well-being through nature. Shinrin Yoku, originating from Japan, means “forest baths” or “taking in the forest atmosphere” and provides the opportunity to reconnect with nature and its benefits, with great potential in Canada. This brief review aims to highlight the potential for the use of Shinrin Yoku in the Canadian context of education and healthcare. Methods: We conducted a narrative literature review including a search of four French and English databases (Google Scholar, Pubmed, Scopus, Cairn) from 1985 to 2017. Then, we classified 26 articles according to three main categories that emerged from the first reading of the abstracts. Results: Benefits of Shinrin Yoku have been classified into physiological, psychological, and environmental categories. We synthesize key benefits of Shinrin Yoku and highlight opportunities to use this alternative intervention by educators and health professionals in Canada. Conclusion: A growing body of research suggests that Shinrin Yoku can have benefits on many aspects of an individual's health and well-being. Given the resources already available in Canada, Shinrin Yoku could be integrated into existing programs and interventions, and could provide another option to educators and healthcare professionals seeking low-risk educational and intervention alternatives for their students and patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".