Surveying people with disabilities: Insights on methods and challenges
Bibliographic record
Abstract
• Advisory Board with disabilities improved survey clarity, accessibility, and relevance. • Modular surveys and accessible formats reduces fatigue and improve data quality. • Diverse recruitment strategies and community partnerships enhance representation and trust. • Fair compensation is vital, but must be balanced with fraud prevention and benefit eligibility concerns. • Transportation research must redefine “accessibility” to reflect real-world barriers faced by people with disabilities. A fundamental challenge in researching people with disabilities lies in the difficulty collecting data representing the lived experience of people with disabilities – this is particularly true with intersectional research on the built environment, transportation, activities of daily community living (ADCLs), and well-being. There are two primary reasons for this data gap: 1) inherent challenges in surveying people with disabilities, and 2) limitations of existing public datasets, which often fail to capture the vast experiences of people with disabilities, particularly in relation to transportation, the built environment of communities, and people with disabilities’ activities of daily community living. This paper provides a reflection on the challenges of gathering survey data from people with disabilities, which leads to these information gaps that are common in disability research. These insights arise from reflecting on a significant interdisciplinary research project undertaken by the authors, including data collection efforts, sampling and data collection methodology, analyzing challenges arising from current survey technologies, and partnering with individuals with disabilities in a meaningful way that acknowledged the importance of their lived experience. Key lessons learned from these data-gathering efforts include the importance of inclusive survey design, effective recruitment strategies, and robust data validation. By highlighting these lessons, this paper aims to improve future disability research and contribute to future data collection efforts that are more inclusive and effective.
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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 | 0.189 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".