Building the foundation for immunization information system interoperability: Lessons from the Canadian context
Bibliographic record
Abstract
Introduction: Accurate, real-time immunization data is integral to the success of immunization programs. Standardized data can be aggregated, analyzed, and leveraged to conduct robust public health surveillance and inform strategic public health planning and prioritization. Standardization is also critical to the interoperability and portability of immunization records within and between jurisdictions. The Canadian Vaccine Catalogue (CVC), which aggregated standardized immunization data from multiple sources, was created to support interoperable immunization systems in Canada. Recommendations: Drawing on our experiences with the CVC and the broader Canadian healthcare system, we propose several recommendations to promote immunization standards adoption, including establishing robust governance processes, bridging the gap between public health and information technology partners, strategizing adoption of standards among electronic medical record vendors, and providing support for standards adoption. Conclusion: The CVC was a critical resource for supporting immunization interoperability in Canada, and provides valuable lessons for other jurisdictions seeking to develop a similar resource.
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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 | 0.026 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".