Assessing patient experiences with a Virtual Triage and Assessment Centre (VTAC): a mixed-methods study using an online survey and semi-structured interviews in Renfrew County, Ontario
Bibliographic record
Abstract
BACKGROUND: 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 alleviate pressure on emergency departments by providing additional and more equitable access to family physicians and allied health professionals. This study's objective was to evaluate patients' experiences with VTAC. METHODS: In this mixed-methods study, we distributed 3,026 surveys, receiving 383 responses that met our inclusion criteria (13%), and conducted 10 semi-structured interviews with Renfrew County residents aged 18 and above who had utilized VTAC at least once since 2023. Survey data were analyzed through descriptive statistics, chi-squared tests, and a multivariate binary logistic regression, while semi-structured interviews were coded and analyzed using reflexive thematic analysis. RESULTS: The majority of survey respondents were aged over 55 (58%), identified as Caucasian (91%) and women (70%), with 76% having college or university-level education. Additionally, 81% were either unattached, or attached to a doctor who was not easily accessible. Our findings demonstrate overall satisfaction with VTAC, with 86% patients reporting that they were satisfied or very satisfied with the program. This was irrespective of demographic characteristics, health status, or appointment modality. In our interviews, four main themes emerged: "Healthcare in Renfrew County", "Accessing VTAC", "VTAC Clinical Care", and "Improving VTAC". These themes underscore major difficulties residents encounter in accessing healthcare in Renfrew County and illustrate that services from VTAC align with a genuine population-level need, contributing to mitigating some of these challenges. CONCLUSION: Renfrew County, like many other underserved regions, is grappling with a crisis of access to healthcare. VTAC addresses this gap by providing timely access to a family doctor. Our findings demonstrate patient acceptability and satisfaction with VTAC, offering insights that could guide the design of similar healthcare programs. This model may also serve as a scalable solution for improving healthcare access in underserved regions facing similar challenges.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".