Accessibility of national parks and other natural protected areas for people with disabilities: a scoping review of the academic literature
Bibliographic record
Abstract
National parks and protected wilderness areas provide benefits through leisure activities to many. Yet, many people with disabilities are unable to experience these benefits because of barriers. Therefore, it is important to understand how to improve access to these spaces for people with disabilities. The objective of this study is to identify barriers and facilitators to accessibility in national parks and protected natural areas. Using a scoping review methodology, we searched eighteen academic databases and found 44 sources meeting our eligibility criteria. Our findings include tables that map the literature by publication year, types of disabilities, specific national parks, park activities, and areas of barriers and facilitators. From these sources, we also identified twelve themes, with thought-provoking ones for us including providing relevant information about the accessibility of parks to potential visitors, effective stakeholder relationships, and facilitating mediated experiences for people who cannot visit parks. A considerable gap in the literature is that many impairments or conditions are either recognized infrequently or not at all. Future research is encouraged to study how a broader range of people with disabilities experience national parks and protected areas.
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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".