How have governments supported citizens stranded abroad due to COVID-19 travel restrictions? A comparative analysis of the financial and health support in eleven countries
Bibliographic record
Abstract
BACKGROUND: In response to the continuing threat of importing novel coronavirus disease (COVID-19), many countries have implemented some form of border restriction. A repercussion of these restrictions has been that some travellers have found themselves stranded abroad unable to return to their country of residence, and in need for government support. Our analysis explores the COVID-19-related information and support options provided by 11 countries to their citizens stranded overseas due to travel restrictions. We also examined the quality (i.e., readability, accessibility, and useability) of the information that was available from selected governments' web-based resources. METHODS: Between June 18 to June 30, 2021, COVID-19-related webpages from 11 countries (Australia, New Zealand, Fiji, Canada, United States of America (USA), United Kingdom (UK), France, Spain, Japan, Singapore, and Thailand) were reviewed and content relating to information and support for citizens stuck overseas analysed. Government assistance-related data from each webpage was extracted and coded for the following themes: travel arrangements, health and wellbeing, finance and accommodation, information needs, and sources. Readability was examined using the Simplified Measure of Gobbledygook (SMOG) and the Flesch Kincaid readability tests; content 'accessibility' was measured using the Web Content Accessibility Guidelines (WCAG) Version 2.1; and content 'usability' assessed using the usability heuristics for website design tool. RESULTS: Ninety-eight webpages from 34 websites were evaluated. No country assessed covered all themes analysed. Most provided information and some level of support regarding repatriation options; border control and re-entry measures; medical assistance; and traveller registration. Only three countries provided information or support for emergency housing while abroad, and six provided some form of mental health support for their citizens. Our analysis of the quality of COVID-19-related information available on a subset of four countries' websites found poor readability and multiple accessibility and usability issues. CONCLUSION: This study uniquely analyses government support for citizens stuck abroad during the COVID-19 pandemic. With large variance in the information and services available across the countries analysed, our results highlight gaps, inconsistencies, and potential inequities in support available, and raise issues pertinent to the quality, accessibility, and usability of information. This study will assist policymakers plan and communicate comprehensive support packages for citizens stuck abroad due to the COVID-19 situation and design future efforts to prepare for global public health emergencies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".