A scoping review of active, participant-centred, digital adverse events following immunization (AEFI) surveillance: A Canadian immunization research network study
Bibliographic record
Abstract
BACKGROUND: Post-licensure adverse events following immunization (AEFI) surveillance is conducted to monitor vaccine safety, such as identifying batch/brand issues and rare reactions, which consequently improves community confidence. The integration of technology has been proposed to improve AEFI surveillance, however, there is an absence of description regarding which digital solutions are successfully being used and their unique characteristics. OBJECTIVES: The objectives of this scoping review were to 1) map the research landscape on digital systems used for active, participant-centred, AEFI surveillance and 2) describe their core components. METHODS: We conducted a scoping review informed by the PRISMA Extension for Scoping Reviews (PRSIMA-ScR) guideline. OVID-Medline, Embase Classic + Embase, and Medrxiv were searched by a medical librarian from January 1, 2000 to January 28th, 2021. Two independent reviewers determined which studies met inclusion based on pre-specified eligibility criteria. Data extraction was conducted using pre-made tables with specific variables by one investigator and verified by a second. RESULTS: Twenty-seven publications met inclusion, the majority of which came from Australia (n = 15) and Canada (n = 6). The most studied active, participant-centred, digital AEFI surveillance systems were SmartVax (n = 8) (Australia), Vaxtracker (n = 7) (Australia), and Canadian National Vaccine Safety (CANVAS) Network (Canada) (n = 6). The two most common methods of communicating with vaccinees reported were short-message-service (SMS) (n = 15) and e-mail (n = 14), with online questionnaires being the primary method of data collection (n = 20). CONCLUSION: Active, participant-centred, digital AEFI surveillance is an area actively being researched as depicted by the literature landscape mapped by this scoping reviewWe hypothesize that the AEFI surveillance approach herein described could become a primary method of collecting self-reported subjective symptoms and reactogenicity from vaccinees, complementing existing systems. Future evaluation of identified digital solutions is necessary to bring about improvements to current vaccine surveillance systems to meet contemporary and future public health needs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".