An innovative mobile data collection technology for public health in a field setting
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».