Proof-of-vaccination credentials for COVID-19 and considerations for future use of digital proof-of-immunization technologies: Results of an expert consultation
Bibliographic record
Abstract
Objective: As part of COVID-19 pandemic control efforts, digital proof-of-vaccination credentials were launched in Canada in 2021-2022 following widespread vaccine availability. Given the controversy over proof-of-vaccination credentials-often colloquially called vaccine or immunization "passports"-it is imperative to document successes, shortcomings, and recommendations for any future uses. Methods: This expert consultation applied inductive qualitative content analysis to online video interviews with key informants whose expertise ranged from ethics to public health to computer science to identify what we can learn from this experience with proof-of-vaccination credentials, and what decision-makers must keep in mind for possible future use of such technologies. Results: There remains a lack of consensus regarding appropriate language and scope for digital proof-of-vaccination technologies, the respective roles of the technology sector versus government in design and implementation, and parameters for future use. However, experts agree on many recommendations, including the importance of clear communication, evidence-based rationale for the use of proof-of-vaccination credentials, multidisciplinary consultation including academic experts and the public, and the importance of pan-Canadian standards for accessibility and interoperability. Identified risks of use that emerged, and should be minimized in the future, include risks of coercion and backlash; threats to access, equity and privacy; and impacts such as costs of the technology and workload burden of enforcement and fraud detection. Conclusions: There is much to learn from this first major use of digital proof-of-vaccination credentials. A full scientific review of the impacts on health and equity should be combined with expert recommendations to create pan-Canadian guidelines for the future use of digital proof-of-vaccination solutions.
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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 | 0.113 | 0.206 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.008 | 0.009 |
| 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".