Barriers and facilitators to improving health care for adults with intellectual and developmental disabilities: what do staff tell us?
Bibliographic record
Abstract
Highlights• We worked with three emergency depart ments and three primary care clinics across Ontario to implement evidence-based practices for high quality care of adults with intellectual and developmental disabilities.• While some sites made considerable progress, others were challenged to make sustainable improve ments.• More successful implementation occurred when sites had strong champions, an interest in this patient group and used electronic prompts and automated point of care tools to embed new practices.• Less successful sites were challenged by staff turnover, low morale and passive endorsement from leadership.• Given these challenges, system level supports are important for wider spread of this intervention.improved practice are the emergency department (ED) and primary care (PC) settings.Both serve as main entry points into the health care system, playing a critical role in providing early and accurate diagnosis, early intervention, and linking individuals to needed community supports.Prior research has shown that although adults with IDD have similar rates of PC AbstractIntroduction: Adults with intellectual and developmental disabilities (IDD) have high rates of morbidity and are less likely to receive preventive care.Emergency departments and primary care clinics are important entry points into the health care system.Improving care in these settings can lead to increased prevention activities, early disease identification, and ongoing management.We studied barriers and facilitators to improving the care of patients with IDD in three primary and three emergency care sites in Ontario.Methods: Data sources included structured implementation logs at each site, focus groups (n = 5) and interviews (n = 8).Barriers and facilitators were coded deductively based on the Consolidated Framework for Implementation Research (CFIR).Synthesis to higher level themes was achieved through review and discussion by the research team.Focus was given to differences between higher and lower implementing sites.Results: All sites were challenged to prioritize care improvement for a small, complex population and varied levels of implementation were achieved.Having national guidelines, using local data to demonstrate need and sharing evidence on value were important engagement strategies.Factors present at higher implementing sites included strong champions, alignment with site mandate, and use of electronic prompts/reminders.Lower implementing sites showed more passive endorsement of the innovation and had lower capacity to implement. Conclusion:Providing effective care for small, complex groups, such as adults with IDD, is critical to improving long-term health outcomes but is challenging to achieve.At a systemic level, funding incentives, access to expertise and improved electronic record systems may enhance capacity.
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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 | 0.018 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| 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".