Lessons learned from the Alberta Border Testing Pilot Program
Bibliographic record
Abstract
Background: During the Coronavirus disease (COVID-19) pandemic, countries implemented border control and quarantine measures to reduce transmission. The Alberta Border Testing Pilot Program (ABTPP) allowed international travellers entering Alberta to reduce their quarantine period following two negative COVID-19 tests. We evaluated participant experiences with the ABTPP and implementation. Method: = 11). We analysed questionnaires using descriptive statistics and analysed interviews using inductive and deductive thematic analysis. We deductively coded focus group data using the 2009 Consolidated Framework for Implementation Research (CFIR). Results: Questionnaires indicated minimal issues with registration forms (91.7%), symptom reports (95.5%), and COVID-19 testing (95.7%). Most respondents (95.1%) expressed willingness to participate in the ABTPP again. Interviews revealed three themes related to participant experience: program efficiency, clarity of information, and requisite effort. Focus groups identified key implementation facilitators including the single health information system, strong stakeholder partnerships, and good communication across partnerships. Barriers included program complexity, implementation timeline, and evolving external context. Discussion: Participants reported high satisfaction with the ABTPP. Border testing programs should have high efficiency, require low effort, and use messaging that is clear and consistent. The effective implementation of border testing programs may be facilitated by strong leadership, adaptability, automated components, good communication, and simple technology. Learnings from participants and staff may help improve the implementation of border control programs for future pandemics or other emergencies. Conclusions: The ABTTP was a novel border control measure during the COVID-19 pandemic. Our evaluation of both participant and staff experiences demonstrated high levels of traveller satisfaction and identified areas for improvement that can inform the development of future border control measures.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".