Researchers’ roadblocks to including people with intellectual and developmental disabilities (DD) in research: Translational science and I/DD program leaders insights
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
People with disabilities in the US are now a health disparities population. Though 25% of US adults have a disability, only 5% of medical research grants are disability related. Knowledge about researchers' perceived barriers to including people with disabilities in research has focused on a single disability/condition and thus has limited translational science applications. Our CTSA's Disability as Difference: Reducing Researcher Roadblocks (D2/R3) project examined such roadblocks towards inclusion of people with intellectual and developmental disabilities (I/DD). I/DDs are broad, heterogeneous conditions that originate in childhood, have varying impact and function, and persist throughout the lifespan. Strategies that mitigate their under-representation in research will likely have general applicability to all disabilities. In D2/R3's first phase we conducted semi-structured interviews with translational science and I/DD program leaders at ten US institutions about perceived barriers and facilitators to including people with I/DD in research. Interviews were held with 25 individuals from partnering Intellectual and Developmental Disabilities Research Centers, University Centers for Excellence in Developmental Disabilities, and Clinical and Translational Science Award programs. Collaborative thematic coding identified key themes as: attitudinal barriers (e.g., assumptions about consent capacity), logistical barriers (e.g., accommodation costs), health disparities, and generalizability concerns. Findings informed development of a survey based on Prosci's ADKAR® model of change management's five components: Awareness, Desire, Knowledge, Ability and Reinforcement. Exclusion appears to stem from researchers' lack of awareness, misconceptions, and knowledge gaps rather than insurmountable obstacles.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it