Using Mobile Technology To Overcome Jurisdictional Challenges To A Coordinated Immunization Policy
Bibliographic record
Abstract
On March 20, 2014, the Government of Canada and the federal Minister of Health announced the release of ImmunizeCanada (ImmunizeCA), a smart phone application (app) designed to both provide accurate information on immunization for Canadians and allow them to track their and their family members’ immunizations. Based on a prototype developed for parents in Ontario and in partnership with the Canadian Public Health Association, our development team received funding from the Public Health Agency of Canada to build a national immunization app. Our task was to build an Apple- and Android-compatible app, containing all 13 provincial/territorial schedules and vaccine information from each jurisdiction in both Canadian official languages (French and English). The application uses demographic information entered by the user and the most recent recommended provincial vaccination schedule to create a custom profile for multiple family members. It allows parents to track and carry their children’s immunizations records on their mobile device. The application also permits the creation of adult-specific schedules and includes information on travel vaccines. It is also possible to sync the app with your smartphone calendar, generate appointment reminders, print or share an immunization record by email, and access answers to common questions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".