Understanding barriers and facilitators to accessibility in the built and natural environment for people with lower limb loss: A qualitative study
Bibliographic record
Abstract
OBJECTIVES: To explore the facilitators and barriers to community accessibility from the perspective of persons with lower limb amputation (LLA). METHODS: A qualitative study using an adapted version of the Photovoice methods described by Wang and Burris was undertaken. Analysis of the interviews and photos was conducted following thematic content analysis. RESULTS: Seven adults with LLA who were ambulating with a prosthesis at the time of the interview participated in the Photovoice interviews. From the interviews with the participants, 3 main themes were developed: (1) current state of accessibility, (2) impact of community inaccessibility, and (3) hope for the future. Participants described what accessibility currently involved in their community (eg, the positive and negative structures) as well as the impact of inaccessibility on their physical and emotional health. Participants also spoke about what improvements they would like to see in the future regarding community accessibility. CONCLUSIONS: By using the Photovoice methods, participants were able to provide tangible examples of what influences their community accessibility. This study highlights the broad range of changes that could provide accessibility opportunities for individuals with LLA from simple changes (eg, adding handrails to arenas) to more complex changes (eg, improvements in parking lot accessibility).
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".