Access to Human Health Benefits of Forests in Rural Low and Middle-Income Countries: A Literature Review and Conceptual Framework
Bibliographic record
Abstract
Forests are increasingly recognized for their beneficial roles in human health. However, there is a debate on how forest health benefits can be accessed equitably, particularly by vulnerable forest-dependent rural communities in low- and middle-income countries (LMICs). Access to forest health benefits is determined by a range of interconnected means, including property rights, as well as natural, physical, human, social, and financial capital. This paper presents a literature review of the roles of means of access in shaping human health effects of forests. Evidence suggests that variations in these means of access are associated with varying ability to access forest health benefits. However, existing evidence is thin, mixed, and weak. A conceptual model is then developed to provide a framework for understanding how means of access moderate the effects of forests on health in rural LMICs to guide the generation of strong evidence. The multiple interconnected factors moderating the health effects of forests at the core of the conceptual framework promote the multisectoral and transdisciplinary approaches needed to enhance equitable access to forest health benefits.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".