Connecting remote populations to public health: the case for a digital immunisation information system in Nunavut
Bibliographic record
Abstract
Despite the best efforts of local healthcare workers and health officials, Nunavut, a large geographical region in Northern Canada, has struggled with outbreaks of vaccine-preventable diseases (VPD). We contend that the implementation of an immunisation information system (IIS) could strengthen prevention and response efforts to this and future outbreaks of vaccine-preventable diseases. Developing an IIS in Nunavut that builds on the existing CANImmunize infrastructure would reduce the cost and complexity of developing a new IIS, and allow Nunavut to benefit from the ongoing efforts to secure data on the CANImmunize platform. Such a system would enable the identification of individuals and subpopulations at highest risk of infection based on vaccine series completion and permit the exploration of the underlying causes of outbreaks in the territory through consideration of demographic and temporal factors. Confirmed high rates of vaccination in the context of an outbreak would indicate potential issues with vaccine efficacy while low rates of vaccination would suggest that efforts should be devoted to increasing vaccine coverage. This approach could also lay the foundation for infrastructure expansion to other remote and/or Indigenous communities where geographical and accessibility issues complicate health care utilisation and monitoring, both in Canada and internationally.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".