Information Sheet 18: Health inequities experienced by people with developmental disabilities
Bibliographic record
Abstract
Developmental disabilities (DDs) are chronic conditions that begin in childhood and are likely to be life-long impacting the ability to live independently as an adult (CDC, 2017). DDs may include Autism spectrum disorder, Down syndrome, intellectual disabilities, Attention deficit hyperactivity disorder, and cerebral palsy, among others. Young persons with DDs experience increased difficulties in accessing quality health care as they transition from pediatric to adult healthcare services. Young persons with DDs have complex health care needs. As they grow older, they are more likely than their peers without disabilities to develop chronic health conditions (Thomas et al., 2011). During emerging adulthood (period from adolescence to young adulthood) these individuals are at increasing risk of developing health problems. During this period, they and their families face increased economic, social, health and mental health related challenges. Studies from various countries, including Canada, found that people with DDs are poorly supported by healthcare systems and services (Fisher, 2004; Krahn et al., 2006; Scheepers et al., 2005; Sullivan et al., 2011). Although nurses are strategically positioned to provide care to individuals with DDs, they are not fully equipped with the skills, awareness, supports, and education for this active care role. The major challenges nurses face in providing good care to this population include time constraints, communication challenges and insufficient education and training (Khanlou et al., 2019)
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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.143 | 0.040 |
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".