An innovative mobile data collection technology for public health in a field setting
Bibliographic record
Abstract
OBJECTIVES: The Canadian Network for Public Health Intelligence (CNPHI) is a secure, web-based scientific informatics and biosurveillance platform that leverages disparate public health information resources and expertise for the direct benefit of local, regional and national decision makers. CNPHI fosters collaboration and consultation through innovation in disease surveillance, intelligence exchange, research and response to protect, promote and support public health. The objective of this article is to present the CNPHI 'on the go' mobile application, and to discuss preliminary evaluation of the technology. The mobile application is intended to enable rapid mobile data collection using both online and offline modes supporting various stages of surveillance and response through the extension of data collection and analysis to the mobile environment. METHODS: Two needs assessment meetings were held with stakeholders representing individuals from government, academia and research institutions, to inform the development of the CNPHI "on the go" mobile application. An initial version of the mobile technology (an "app") was developed and piloted by end-users with expertise in the field. Two focused pilots were conducted to test the technology: Pilot 1: 17-7-2017 to 21-11-2017 (6 participants); Pilot 2: 25-7-2017 to 15-9-2017 (2 participants). An initial consultation was held with the project leads to identify data elements for mobile data collection. A custom data collection form was designed using CNPHI's Web Data technology for each pilot, which was then made available through the mobile app. The technology was assessed using feedback received during each pilot as well as through a survey that was conducted at the conclusion of pilots. RESULTS: Pilot participants reported that the mobile technology allowed seamless data collection, data management and rapid information sharing. Participants also reported that the entire process was seamless, simple, efficient, and that fewer steps were required for data collection and management. Further, significant efficiencies were gained by directly entering information using the mobile app without having to transfer handwritten information into an electronic database. An overall positive experience was reported by participants from both pilots. DISCUSSION: Literature suggests that traditional methods of surveillance and data collection using a paper based methodology pose many challenges such as data loss and duplication, difficulty in managing the database, and lack of timely access to the data. Accurate and rapid access is critical for public health professionals in order to effectively make decisions and respond to public health emergencies. Results show that the CNPHI "on the go" app is well poised to address some of the suggested challenges. A limitation of this study was that sample size for pilot participation was small for capturing overall feedback on the readiness of the technology for integration into regular surveillance activities and response procedures. CONCLUSIONS: CNPHI "on the go" is a customizable technology developed within an already thriving collaborative CNPHI platform used by public health professionals, and performs well as a tool for rapid data collection and secure information sharing.
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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 | 0.005 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".