Bibliographic record
Abstract
This literature review examines the current state of inclusive instrument design, highlighting a significant gap in the scholarly discourse. Research instruments, such as surveys, interview protocols, and usability tests, are typically developed with neuro-typical or non-disabled participants in mind. Through an exploratory approach, this critical review gathers literature from disability studies, education, information science, and social sciences to provide a broader perspective on inclusive instrument design. Key findings identify gaps, challenges, and recommended practices for accessibility and inclusivity in research study instruments and experiences. Three broad themes were identified, including frameworks and methodologies for accessibility or instrument design, challenges related to accessibility in instrument design, and general recommendations for inclusive instrument design and instrument accessibility. While some studies have adapted instruments for participants with disabilities, few have intentionally included these perspectives in the design process. Addressing this gap, this review presents strategies that can benefit diverse research study participants, regardless of ability. These insights support the adoption of inclusive survey, interview, and usability study design practices. By enhancing research experiences for individuals with disabilities, impairments, and chronic conditions, the study suggests that universal, human-centered design can improve user participation in research and its outcomes.
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 | 0.032 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".