A Human-Centered Platform for HIV Infection Reduction in New York: Development and Usage Analysis of the Ending the Epidemic (ETE) Dashboard
Notice bibliographique
Résumé
BACKGROUND: Dashboards have been increasingly used in clinic-based interventions, such as clinical performance improvement and monitoring risk of hospital readmissions, and are now gaining traction in population-based interventions, especially in disease assessment. OBJECTIVE: We describe the design, development, and usage analysis of a geovisualization dashboard, the Ending the Epidemic (ETE) Dashboard. The ETE dashboard is a tool developed to track New York's progress towards achieving the goal of its ETE Initiative, to reduce new HIV infections from 3000 per year to 750 per year by the end of 2020. METHODS: The ETE dashboard was adapted from an existing human-centered geovisualization platform, SanaViz, an Internet-enabled, interactive app incorporating principles of human-centered design and cognitive fit theory to enhance visual exploration of population health data. Usage evaluation of the ETE geovisualization dashboard was conducted using Google Analytics over a 4-week period from March 19 to April 18, 2016. The aim was to monitor user activity and analyze traffic on the ETE dashboard using evidence-based metrics that can provide adequate feedback to enhance its utilization. Usage was characterized based on three metrics: (1) number of unique visits to each page, (2) average time on each page in seconds, and (3) page bounce rate (ie, percentage of visits where user left the site immediately after viewing just a single page). Further analysis was also conducted by cross-tabulating specific usage metrics. RESULTS: Of 860 sessions, 324 sessions were initiated by unique users (37.7%). The most common acquisition channels included direct source (353/860, 41.0%); followed by referral traffic (340/860, 39.5%) and organic search (134/860, 15.5%). Usage statistics indicate that for the 860 sessions initiated by both new and returning users, the average viewing time was 8 minutes, 51 seconds, and the bounce rate was 46%. These statistics reflect positive results given that prior literature estimates an average session duration of 10-20 seconds and a bounce rate of 40-60% for most websites. Key findings from our study showed that the highest bounce rates were seen for the "About" page of the ETE website (65%), which describes the ETE initiative. The lowest bounce rates were seen for the ETE blog page (17%), consistent with prior research on website usage metrics that indicate that blog pages are often the most frequently viewed pages on Web portals. CONCLUSIONS: Our findings reveal the potential of Google Analytics as a tool to enhance user traffic and performance of the ETE geovisualization platform by using feedback from regular monitoring of key parameters including page bounce rates and average time on page. It also identifies the need for a follow-up usability assessment of the system.
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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 | 0,007 | 0,017 |
| 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 | 0,003 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».