Apps for immunization: Leveraging mobile devices to place the individual at the center of care
Bibliographic record
Abstract
Mobile technology and applications (apps) have disrupted several industries including healthcare. The advantage of apps, being personally focused and permitting bidirectional communication, make them well suited to address many immunization challenges. As of April 25, 2015 searching the Android app store with the words 'immunize app' and 'immunization app' in Canada yielded 225 apps. On the Apple App Store a similar search produced 98 results. These include apps that provide immunization related information, permit vaccine tracking both for individuals and for animals, assist with the creation of customized schedules and identification of vaccine clinics and serve as sources of education. The diverse functionality of mobile apps creates the potential for transformation of immunization practice both at a personal level and a system level. For individuals, mobile apps offer the opportunity for better record keeping, assistance with the logistics of vaccination, and novel ways of communicating with and receiving information from public health officials. For the system, mobile apps offer the potential to improve the quality of information residing in immunization information systems and program evaluation, facilitate harmonization of immunization information between individuals, health care providers and public health as well as reduce vaccine hesitancy. As mobile technology continues to rapidly evolve there will emerge new ways in which apps can enhance immunization practice.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".