Understanding the secondary outcomes of international travel measures during the covid-19 pandemic: a scoping review of social impact evidence
Bibliographic record
Abstract
BACKGROUND: Assessment of the effective use of international travel measures during the COVID-19 pandemic has focused on public health goals, namely limiting virus introduction and onward transmission. However, risk-based approaches includes the weighing of public health goals against potential social, economic and other secondary impacts. Advancing risk-based approaches thus requires fuller understanding of available evidence on such impacts. METHODS: We conducted a scoping review of existing studies of the social impacts of international travel measures during the COVID-19 pandemic. Applying a standardized typology of travel measures, and five categories of social impact, we searched 9 databases across multiple disciplines spanning public health and the social sciences. We identified 26 studies for inclusion and reviewed their scope, methods, type of travel measure, and social impacts analysed. RESULTS: The studies cover a diverse range of national settings with a strong focus on high-income countries. A broad range of populations are studied, hindered in their outbound or inbound travel. Most studies focus on 2020 when travel restrictions were widely introduced, but limited attention is given to the broader effects of their prolonged use. Studies primarily used qualitative or mixed methods, with adaptations to comply with public health measures. Most studies focused on travel restrictions, as one type of travel measure, often combined with domestic public health measures, making it difficult to determine their specific social impacts. All five categories of social impacts were observed although there was a strong emphasis on negative social impacts including family separation, decreased work opportunities, reduced quality of life, and inability to meet cultural needs. A small number of countries identified positive social impacts such as restored work-life balance and an increase in perceptions of safety and security. CONCLUSIONS: While international travel measures were among the most controversial interventions applied during the COVID-19 pandemic, given their prolonged use and widespread impacts on individuals and populations, there remains limited study of their secondary impacts. If risk-based approaches are to be advanced, involving informed choices between public health and other policy goals, there is a need to better understand such impacts, including their differential impacts across diverse populations and settings.
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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 | 0.037 | 0.181 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.011 |
| Bibliometrics | 0.027 | 0.020 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".