The well-being of Homo sapiens in forests: A scoping review of frameworks and indicators
Bibliographic record
Abstract
Nature exposure holds the potential to enhance human well-being. However, due to the diversity of disciplines and approaches, planners, managers and decision-makers can face challenges in navigating the supporting literature. This review provides a summary of the frameworks and indicators used to capture the relationship between forests and human well-being, thereby enabling readers to consider human well-being in their work. A scoping review was performed with a systematic approach on Scopus and Web of Science databases. Altogether 130 studies were summarized into thematic categories. The reviewed frameworks point to a variety of aspects of the complex relationship. No gold standard on framework or indicator for forests and well-being exists and the choice can be guided by the practitioner’s needs. However, a number of frameworks could inform forest practitioners about factors that influence how people derive well-being from forests. Practitioners could consider how to increase opportunities for connectedness to nature and social connection in the natural spaces they manage. They could also collaborate with other agencies to increase public knowledge and confidence to engage with nature. Considering barriers and inequality could make the benefits accessible to a wider range of the population. Finally, it is important to consider how to build healthy and beneficial relationships between people and nature. Several articles present tools specifically for forest planners and managers. However, more research is needed to strengthen the causal evidence that most insights build on.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".