Circles of care for people with intellectual and developmental disabilities: Communication, collaboration, and coordination.
Bibliographic record
Abstract
OBJECTIVE: To review health information exchange (HIE) processes that affect the health of people with intellectual and developmental disabilities (IDD) and to suggest practical tips and strategies for communicating, collaborating, and coordinating in the primary care setting. SOURCES OF INFORMATION: The "Primary care of adults with intellectual and developmental disabilities. 2018 Canadian consensus guidelines" literature review and interdisciplinary input. MAIN MESSAGE: Disparities exist between the provision of health care for the general population and that for people with IDD. These disparities are due in part to gaps in HIE. Health information exchange involves documenting, collecting, and disseminating a patient's health information. In exploring ways to improve HIE for people with IDD, the communication skills of the family physician are considered in the context of the triad that includes the patient, his or her caregivers, and the family physician. The framework of the Patient's Medical Home is used in exploring these processes, and various strategies are offered for communicating, collaborating, and coordinating health care that can be implemented by family physicians in order to narrow the gaps in care that exist for people with IDD. CONCLUSION: Improvements in HIE by communicating, collaborating, and coordinating health care better will improve health outcomes for people with IDD.
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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 | 0.009 | 0.041 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".