Cross-border healthcare: A review and applicability to North America during COVID-19
Bibliographic record
Abstract
Cross-border healthcare is an international agreement for the provision of out of country healthcare for citizens of partnered countries. The European Union (EU) has established itself as a world leader in cross-border healthcare. During the Coronavirus disease of 2019 (COVID-19) pandemic, the EU used this system to maximize utilization of resources. Countries with capacity accepted critically ill patients from overwhelmed nations, borders remained open to healthcare workers and those seeking medical care in an effort to share the burden of this pandemic. Significant research into the challenges and successes of cross-border healthcare was completed prior to COVID-19, which demonstrated significant benefit for patients. In North America, the response to the COVID-19 crisis has been more isolationist. The Canada-United States border has been closed and bans placed on healthcare workers crossing the border for work. Prior to COVID-19, cross-border healthcare was rare in North America despite its need. We reviewed the literature surrounding cross-border healthcare in the EU, as well as the need for a similar system in North America. We found the EU cross-border healthcare agreements are generally mutually beneficial for participating countries. The North American literature suggested a cross-border healthcare system is feasible. A number of challenges could be identified based on the EU experience. A prior agreement may have been beneficial during the COVID-19 crisis as many Canadian healthcare institutions-maintained capacity to accept critically ill patients.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".