The Benefits and Pitfalls of Virtual Primary Care for Patients with Intellectual and Developmental Disabilities
Bibliographic record
Abstract
The COVID-19 pandemic led to an abrupt and unprecedented increase in the delivery of virtual primary health care. Adults with intellectual and developmental disabilities (IDD) have complex health care needs and little is known about the value and appropriateness of virtual care for this patient population. The aim of this dissertation was to learn about the impact of virtual care delivery on access and quality of primary care for patients with IDD. This dissertation included 3 studies. Study 1 was a scoping review of the prior literature on virtual health care for adults IDD. Twenty-two studies were identified that met inclusion criteria, of which 12 reported findings on access to care. Participants generally reported high acceptability of virtual care and results on effectiveness were positive, though conclusions were limited by small sample sizes. Challenges identified included internet quality and technical skill. Studies 2 and 3 used a qualitative approach to explore multiple perspectives on virtual primary care for adults with IDD in Ontario during the pandemic. Semi-structured interviews were conducted with 38 participants including: 11 adults with IDD, 13 family caregivers, 5 support staff and 9 primary care physicians. Study 2 used Levesque’s access to care framework as an organizing structure to synthesize themes related to access to care. An overarching finding was that accessibility needs are not static and vary according to patient characteristics, patient context, caregiver characteristics, the service context, and the reason for a particular primary care visit. Study 3 explored the impact of virtual care on patient-provider communication. Four elements of communication were identified that were impacted by virtual care: (1) the ability to hear other participants and have the time and space to speak; (2) use of nonverbal communication; (3) the ability to form trusting relationships; and (4) patient engagement in the virtual appointment. This dissertation supports the need for a flexible patient-driven approach to primary care that includes in-person, phone and video options. This approach must be supported by the appropriate infrastructure, resources and incentives to ensure that the potential benefits of virtual care can be fully realized for all patients.
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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 | 0.020 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".