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Record W4412754034 · doi:10.1080/02508281.2025.2525167

Exploring VFR travel intentions during COVID-19: insights for resiliency planning

2025· article· en· W4412754034 on OpenAlexaff
Tom Griffin, Daniel Guttentag, Wayne W. Smith, Stephen W. Litvin

Bibliographic record

VenueTourism Recreation Research · 2025
Typearticle
Languageen
FieldMedicine
TopicTravel-related health issues
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)TourismBusinessEnvironmental planningPsychologyGeographyMedicineVirology

Abstract

fetched live from OpenAlex

This research note offers empirical insights regarding the resiliency of visiting friends and relatives (VFR) travel during a crisis. Using data collected during the COVID-19 pandemic from 9,537 U.S. residents, this study finds that VFR trips were associated with higher travel intentions and lower cancellation rates than Leisure/Vacation trips. These patterns were particularly pronounced among more risk-averse travellers, as segmented using Plog’s 1974 model of tourist types published in Cornell Hotel and Restaurant Quarterly. These findings suggest that VFR represents a relatively stable form of visitor demand during disruptions, reinforcing its relevance in destination resiliency planning. The results highlight the value of further research into how residents, as hosts, can contribute to recovery strategies in future crises.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.685
Threshold uncertainty score0.876

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.411
GPT teacher head0.509
Teacher spread0.098 · 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 routes1
Has abstractyes

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