Evaluating Patient Experiences with a Virtual Triage and Assessment Centre in Renfrew County, Ontario, Canada
Bibliographic record
Abstract
Context: In March 2020, the Renfrew County Virtual Triage and Assessment Centre (VTAC) was launched as a large-scale, innovative, hybrid healthcare program. VTAC aims to provide timely and equitable access to care in a predominantly rural region facing deep-rooted healthcare challenges. Past assessments, using the quintuple aim framework, showed positive results in key areas like clinical impact, cost, and provider experiences. However, an important aspect yet to be explored is how patients experience the program. Objective: The aim of this study was to evaluate patients’ experiences with VTAC. Study Design and Analysis: Collaborating with three patient partners, we employed a sequential explanatory mixed-methods approach. In Phase 1, we administered a 37-question online survey to 400 residents to assess patients’ experiences with VTAC’s various visit modalities. In Phase 2, building upon the survey results, we conducted 10 semi-structured interviews. The survey results were analyzed using descriptive statistics, chi-square tests, and logistic regression, while the interviews were coded and analyzed using thematic analysis. Setting or Dataset: VTAC operates in Renfrew County, the largest county in the province of Ontario. The county lacks walk-in clinics, resulting in overburdened emergency departments. Additionally, the region has higher-than-average rates of chronic mental and physical illnesses, low socioeconomic status, barriers due to travel distance, and a concerning unattachment rate of 20-25%. Population Studied: Adult residents of Renfrew County who had at least one encounter with VTAC. Intervention/Instrument: Surveys and semi-structured interviews. Outcome Measures: Patient Experiences with VTAC. Results: Throughout surveys and interviews, two key findings emerged: Firstly, participants expressed widespread satisfaction with VTAC. This was irrespective of demographic characteristics, health status or appointment modality. Secondly, participants reported significant challenges in accessing care in Renfrew County, an issue which even extends to those with formal attachment to a provider. Conclusion: Our mixed-methods study reinforces the idea that VTAC can be a valuable tool for addressing the access-to-care crisis facing Renfrew County. Furthermore, the high patient satisfaction identified in our study underscores the program’s acceptability. VTAC’s core model could serve as a blueprint for the design of future healthcare programs.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".