“Can you hear me now?”: a qualitative exploration of communication quality in virtual primary care encounters for patients with intellectual and developmental disabilities
Bibliographic record
Abstract
BACKGROUND: High quality communication is central to effective primary care. The COVID-19 pandemic led to a dramatic increase in virtual care but little is known about how this may affect communication quality. Adults with intellectual and developmental disabilities (IDD) can experience challenges communicating or communicate in non-traditional ways. This study explored how the use of virtual modalities, including telephone and video, affects communication in primary care interactions for patients with IDD. METHODS: This qualitative descriptive study included semi-structured interviews with a multi-stakeholder sample of 38 participants, including 11 adults with IDD, 13 family caregivers, 5 IDD support staff and 9 primary care physicians. Interviews were conducted in Ontario, Canada between March and November 2021 by video-conference or telephone. A mixed inductive and deductive thematic analysis approach was used to code the data and identify themes. Themes were reviewed and refined with members of each stakeholder group. RESULTS: Four elements of communication were identified that were affected by virtual care: (1) patient engagement in the virtual appointment; (2) the ability to hear other participants and have the time and space to be heard; (3) the ability to use nonverbal communication strategies; and (4) the ability to form trusting relationships. In some cases, the virtual platform hindered these elements of communication. Video offered some advantages over telephone to support nonverbal communication, and stimulate engagement; though this could be limited by technical challenges. For adults with IDD who find it difficult to attend in-person appointments, virtual care improved communication quality by allowing them to participate from a space where they were comfortable. CONCLUSION: Though there are circumstances in which virtual delivery can improve communication for patients with IDD, there are also challenges to achieving high quality patient-provider communication over telephone and video. Improved infrastructure and training for providers, patients and caregivers can help improve communication quality, though in some cases it may never be appropriate. A flexible patient-centred approach is needed that includes in-person, telephone and video options for care.
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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 | 0.023 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".