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Record W4407043182 · doi:10.1186/s12875-025-02719-y

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

2025· article· en· W4407043182 on OpenAlexaffabout
Antoine St-Amant, Cayden Peixoto, Dez Bair-Patel, Martha Heideman, Kayla Menkhorst, Jonathan Fitzsimon

Bibliographic record

VenueBMC Primary Care · 2025
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsUniversity of OttawaInstitut du Savoir Montfort
Fundersnot available
KeywordsThematic analysisDescriptive statisticsHealth careTriageMedicineScale (ratio)Family medicineLogistic regressionPopulationInclusion (mineral)NursingPsychologyMedical educationQualitative researchEnvironmental healthMedical emergencyGeographySocial psychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.254
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.071
GPT teacher head0.406
Teacher spread0.336 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes2
Has abstractyes

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