The Mental Health of Adults with Developmental Disabilities in Ontario: Lessons from Administrative Health Data
Bibliographic record
Abstract
Adults with developmental disabilities have increased rates of mental illness and addiction, in addition to being more likely to experience physical health issues.This can lead to high rates of hospital and community-based healthcare.Population-based administrative health data can help in identifying the extent of problems experienced and target areas for policy and practice changes. The IssueMental healthcare costs total over $50 billion a year in Canada, affecting more than 6.7 million people (Mental Health Commission of Canada 2013;Ratnasingham et al. 2012).Adults with developmental disabilities (DDs) such as Down syndrome or autism have a high rate of mental health issues and face significant barriers when accessing appropriate and timely mental healthcare (Lunsky et al. 2007, Lunsky et al. 2013c).Such challenges can lead to additional problems for them and their families.There is currently no national strategy or approach for addressing the mental health needs of this population, and the training of mental healthcare professionals in the field of DDs is extremely limited.The situation is further complicated by the fact that the required services and supports for this group are spread across the health and social service sectors.Just how big an issue this is in our country is difficult to know with limited data.Up until recently, identifying persons with DDs in existing databases was not routinely done, leading to a lack of data and knowledge regarding their mental health diagnoses and mental health service use.In Ontario, we had a unique opportunity to explore mental health concerns and service utilization patterns by linking data from social services to data from health services at the Institute for Clinical Evaluative Sciences (Lin et al. 2014).This initiative, which began in 2010, is part of the Health Care Access Research and Developmental Disabilities Program (H-CARDD; https://www.porticonetwork.ca/web/hcardd),a partnership between scientists, clinicians, policy makers, patients and families.We summarize here findings from the program thus far that have important implications for mental health policy and service delivery. Key Findings Greater prevalence of mental illness and addiction
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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 | 0.013 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".