Measuring adult health and well-being outcomes associated with nature contact in protected areas: A scoping review
Bibliographic record
Abstract
BACKGROUND: Growing evidence shows various health and well-being benefits from nature contact in protected and conserved areas. However, methods to measures these outcomes lack systematic identification, critical appraisal, and synthesis. OBJECTIVES: This review aims to identify the instruments used to measure mental health and well-being outcomes of adults associated with direct nature contact in protected areas. Additionally, it examines the extent to which studies detail the measurement quality of instruments used, as an indicator of methodological transparency and rigor in reporting. METHODS: Following PRISMA-ScR guidelines, a scoping review was conducted by searching eight scholarly databases on January 4, 2023, with an update on December 11, 2024. The search focused on three domains: (1) protected areas; (2) nature engagement; and (3) mental health and well-being. Studies were screened based on the inclusion criteria and included only English language studies with adult participants (aged 18+ years). Data were extracted and reported as a narrative synthesis. RESULTS: A total of 51 studies met the inclusion criteria, revealing 71 distinct measurement instruments for assessing mental health and well-being. Of these, 35 % were established quantitative, 41 % were non-established quantitative, and 24 % were qualitative instruments. Additionally, 11 sub-concepts of mental health and well-being were identified. The methodological quality of studies varied, with 67 % showing evidence of 1-2 quality dimensions. CONCLUSIONS: Despite growing interest in nature's health benefits, there is a lack of consistent measurement instruments to assess these outcomes. This review identifies strengths and weaknesses in current approaches and offers recommendations for future research.
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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 | 0.027 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".