News reporting on travel restrictions between Canada and the US during the COVID-19 pandemic: An equity-based analysis
Bibliographic record
Abstract
The worldwide use of different types of travel measures during the COVID-19 pandemic was unprecedented. However, there remains need for fuller understanding of how to apply travel measures for public health benefit, while taking account of their social and economic impacts, particularly on equity-deserving populations. This paper presents an equity analysis of 41 online news articles about travel restrictions (as one type of travel measure) on cross-border travel between Canada and the United States during the period from March 2020 and May 2023. Applying conceptual frameworks to understand health equity and social justice, we ask whether and how different forms of inequity associated with travel restrictions were presented in the media. Results reveal that certain inequities (e.g. those experienced by older adults, caregivers, etc.) were more often described in news coverage than others (i.e. racialized populations), with many key equity deserving groups not mentioned at all (i.e. people with disabilities). News reports depict travel restrictions causing disproportionate economic and social harms on low-income residents, tourism-dependent regions and cross-border communities, including certain Indigenous communities. Public concerns underscored what was perceived to be unsystematic and unpredictable policy implementation characterised, at times, by a lack of compassion and based on the discretionary powers of border officials. This analysis identifies how news reports shed light and frame certain inequities, for some individuals and populations, arising from Canada-U.S. travel restrictions. It highlights the need to amplify equity within future responses to global public health emergencies, but also key gaps in knowledge. The paper contributes fuller understanding of how travel restrictions interact with health, social and economic inequities, and puts forth policy recommendations about the use of travel measures during pandemics.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.013 | 0.019 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".