Virtual care policy recommendations for patient-centred primary care: findings of a consensus policy dialogue using a nominal group technique
Bibliographic record
Abstract
Background The development of new virtual care technologies (including telehealth and telemedicine) is growing rapidly, leading to a number of challenges related to health policy and planning for health systems around the world. Methods We brought together a diverse group of health system stakeholders, including patient representatives, to engage in policy dialogue to set health system priorities for the application of virtual care in the primary care sector in the Province of Ontario, Canada. We applied a nominal group technique (NGT) process to determine key priorities, and synthesized these priorities with group discussion to develop recommendations for virtual care policy. Methods included a structured priority ranking process, open-ended note-taking, and thematic analysis to identify priorities. Results Recommendations were summarized under the following themes: (a) identify clear health system leadership to embed virtual care strategies into all aspects of primary and community care; (b) make patients the focal point of health system decision-making; (c) leverage incentives to achieve meaningful health system improvements; and (d) building virtual care into streamlined workflows. Two key implications of our policy dialogue are especially relevant for an international audience. First, shifting the dialogue away from technology toward more meaningful patient engagement will enable policy planning for applications of technology that better meet patients' needs. Second, a strong conceptual framework on guiding the meaningful use of technology in health care settings is essential for intelligent planning of virtual care policy. Conclusions Policy planning for virtual care needs to shift toward a stronger focus on patient engagement to understand patients' needs.
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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 | 0.273 | 0.369 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.019 | 0.015 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.008 | 0.009 |
| 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".