Examining experiences and system impacts of publicly funded episodic virtual care: protocol for a cross-provincial mixed methods study
Bibliographic record
Abstract
INTRODUCTION: Health systems are under pressure as one in five Canadians have no regular place for primary care, with many experiencing substantial travel times and delays in accessing care. In the context of these urgent needs, platforms for virtual care offer immediate access to care in 'walk-in' style format, with limited continuity for ongoing health needs or coordination with other health services. We refer to these services as episodic virtual care (EVC), to distinguish them from virtual services offered in longitudinal primary care. The governments of Nova Scotia (NS) and New Brunswick (NB) both offer publicly funded EVC and offer a unique opportunity for research.The overarching goal of this work is to learn from the implementation of EVC in NS and NB to understand experiences and system impacts, includingWhat are patient perceptions and experiences of EVC and how do these differ by patient characteristics?What are the characteristics of patients who use EVC and of clinicians who deliver it?What are the system impacts of EVC? METHODS AND ANALYSIS: We will use a cross-sectional survey conducted through an online questionnaire to explore patient perceptions and experiences with EVC. We will also examine how these differ based on the type of care needed, age, gender, residence (urban or rural), immigration and language preference. We will use linked administrative data and quasi-experimental analysis to assess the impacts of EVC on visits to community-based primary care (including in-person walk-in clinics), emergency department visits, prescriptions and referrals for other health services like laboratory testing, imaging and consulting specialist physicians. ETHICS AND DISSEMINATION: This proposal has been reviewed and received approval from the Nova Scotia Health Research Ethics Board. Findings will identify the impacts and trade-offs in the deployment of EVC, which will inform primary care planning. In addition to traditional academic publications and information provided to primary care patients/the public, this study will inform decision-makers across multiple jurisdictions as they contend with the challenge of meeting patients' immediate care needs for access to primary care, while seeking to improve coordination and integration of systems as a whole.
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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 | 0.070 | 0.047 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.068 | 0.012 |
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".