Researchers’ roadblocks to including people with intellectual and developmental disabilities (DD) in research: Translational science and I/DD program leaders insights
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.
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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 | 0.307 | 0.267 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.030 | 0.035 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.005 | 0.032 |
| Research integrity | 0.007 | 0.020 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".