Immunization and technology among newcomers: A needs assessment survey for a vaccine-tracking app
Bibliographic record
Abstract
OBJECTIVES: Newcomers experience unique challenges with respect to vaccination. These challenges are compounded by the need to navigate complex vaccination catch-up schedules upon arrival in their new home countries. Our group has pioneered the development of CANImmunize, a free, bilingual, pan-Canadian digital application designed to empower individuals to manage their vaccination records. To inform how a vaccine tracking app such as CANImmunize might be tailored to meet the unique needs of newcomers, this study sought to determine commonly spoken languages, technology use, and current methods of vaccine tracking among recent newcomers to Canada. METHODS: Government-assisted refugees attending a health clinic in Ottawa, Canada were invited to complete a 17-question needs assessment survey. The survey captured data on household demographics, spoken languages, country of origin, technology use and methods used to track vaccination history. RESULTS: 50 newcomers completed the needs assessment survey. Arabic was the predominant language spoken by surveyed individuals. Although 92% of participants owned a smartphone, the majority did not actively use digital health applications. 18 (36%) participants reported being vaccinated before arriving in Canada. 27 (54%) participants were parents, 23 of whom reported that their children were vaccinated prior to arrival in Canada. 38 (76%) participants indicated that they would use a vaccine tracking app such as CANImmunize if it were translated into their primary language of communication. CONCLUSIONS: The results of our study indicate that mobile technology may be a useful tool to help newcomer families stay on track with provincial and territorial immunization schedules.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".