A guided photovoice approach to explore experts with disabilities’ lived experiences of accessibility and usability while engaging in active transportation in a rural Canadian community
Bibliographic record
Abstract
Individuals with disabilities residing in rural regions, such as Nova Scotia, face greater barriers to accessibility including reduced opportunities to access and use of essential community services, modes of transportation, and spaces. This study employed a qualitative, guided photovoice approach to understand the perceived barriers and facilitators to active transport based on the lived experiences of 12 experts with a range of disabilities (intellectual, physical, and visual) from a single rural community in Nova Scotia, Canada. Five themes emerged through thematic and comparative analysis of 144 captured photographs and related comments: 1. Accessibility and Usability of the Built Environment 2. Feelings of Safety, 3. Wayfinding, 4. Inclusive Community Spaces, and 5. Beautification. Findings reinforced the need for future research and public policy initiatives to include the voice of experts with disability, and their site-specific knowledge of what makes a rural community inclusive, accessible and useable for people of all ability levels. • Inclusive research design to ensurethe lived experiences of all community members are considered. • This study provides site specific planning information for a rural community in Nova Scotia. • Offers insight to barriers and facilitators to accessibility and active transport in a rural built environment. • A successful use of a guided photovoice methodology for people with disabilities, leading to policy changes through knowledge translation.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| 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".