Improving vaccine registries through mobile technologies: a vision for mobile enhanced Immunization information systems
Bibliographic record
Abstract
Immunization registries or information systems are critical to improving the quality and evaluating the ongoing success of immunization programs. However, the completeness of these systems is challenged by a myriad of factors including the fragmentation of vaccine administration, increasing mobility of individuals, new vaccine development, use of multiple products, and increasingly frequent changes in recommendations. Mobile technologies could offer a solution, which mitigates some of these challenges. Engaging individuals to have more control of their own immunization information using their mobile devices could improve the timeliness and accuracy of data in central immunization information systems. Other opportunities presented by mobile technologies that could be exploited to improve immunization information systems include mobile reporting of adverse events following immunization, the capacity to scan 2D barcodes, and enabling bidirectional communication between individuals and public health officials. Challenges to utilizing mobile solutions include ensuring privacy of data, access, and equity concerns, obtaining consent and ensuring adoption of technology at sufficiently high rates. By empowering individuals with their own health information, mobile technologies can also serve as a mechanism to transfer immunization information as individuals cross local, regional, and national borders. Ultimately, mobile enhanced immunization information systems can help realize the goal of the individual, the healthcare provider, and public health officials always having access to the same immunization information.
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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 | 0.024 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".