An evaluation of the feasibility and usability of a proof of concept mobile app for adverse event reporting post influenza vaccination
Bibliographic record
Abstract
The Canadian National Vaccine Safety network (CANVAS) gathers and analyzes safety data on individuals receiving the influenza vaccine during the early stages of annual influenza vaccination campaigns with data collected via participant surveys through the Internet. We sought to examine whether it was feasible to use a mobile application (app) to facilitate AEFI reporting for the CANVAS network. To explore this, we developed a novel smartphone app, recruited participants from a hospital influenza immunization clinic and by word of mouth and instructed them to download and utilize the app. The app reminded participants to complete the CANVAS AEFI surveillance surveys ("AEFI surveys") on day 8 and 30, a survey capturing app usability metrics at day 30 ("usability survey") and provided a mechanism to report AEFI events spontaneously throughout the whole study period. All survey results and spontaneous reports were recorded on a privacy compliant, cloud server. A software plug-in, Lookback, was used to record the on-screen experience of the app sessions. Of the 76 participants who consented to participate, 48(63%) successfully downloaded the app and created a profile. In total, 38 unique participants completed all of the required surveillance surveys; transmitting 1104 data points (survey question responses and spontaneous reports) from 83 completed surveys, including 21 usability surveys and one spontaneous report. In total, we received information on new or worsening health conditions after receiving the influenza vaccine from 11(28%) participants. Of the usability survey responses, 86% agreed or strongly agreed that they would prefer to use a mobile app based reporting system instead of a web-based system. The single spontaneous report received was from a participant who had also reported using the Day 8 survey. Of Lookback observable sessions, an accurate transmission proportion of 100% (n=290) was reported for data points. We demonstrated that a mobile app can be used for AEFI reporting, although download and survey completion proportions suggest potential barriers to adoption. Future studies should examine implementation of mobile reporting in a broader audience and impact on the quality of reporting of adverse events following immunization.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".