The digital immunization system of the future: imagining a patient-centric, interoperable immunization information system
Bibliographic record
Abstract
To ensure the effectiveness of increasingly complex immunization programs in upper-middle and high-income settings, comprehensive information systems are needed to track immunization uptake at individual and population levels. The maturity of cloud systems and mobile technologies has created new possibilities for immunization information systems. In this paper, we describe a vision for the next generation of digital immunization information systems for upper-middle and high-income settings based on our experience in Canada. These systems center on the premise that the public is engaged and informed about the immunization process beyond their interaction with primary care, and that they will be a contributor and auditor of immunization data. The digital immunization system of the future will facilitate reporting of adverse events following immunization, issue digital immunization receipts, permit identification of areas of need and allow for delivery of interventions targeting these areas. Through features like immunization reminders and targeted immunization promotion campaigns, the system will reduce many of the known barriers that influence immunization rates. In light of the global COVID-19 pandemic, adaptive digital public health information systems will be required to guide the rollout and post-market surveillance of the SARS-CoV-2 vaccine.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".