The Solutions in Health Analytics for Rural Equity Across the Northwest (SHARE-NW) Dashboard for Health Equity in Rural Public Health: Usability Evaluation
Notice bibliographique
Résumé
BACKGROUND: Given the dearth of resources to support rural public health practice, the solutions in health analytics for rural equity across the northwest dashboard (SHAREdash) was created to support rural county public health departments in northwestern United States with accessible and relevant data to identify and address health disparities in their jurisdictions. To ensure the development of useful dashboards, assessment of usability should occur at multiple stages throughout the system development life cycle. SHAREdash was refined via user-centered design methods, and upon completion, it is critical to evaluate the usability of SHAREdash. OBJECTIVE: This study aims to evaluate the usability of SHAREdash based on the system development lifecycle stage 3 evaluation goals of efficiency, satisfaction, and validity. METHODS: Public health professionals from rural health departments from Washington, Idaho, Oregon, and Alaska were enrolled in the usability study from January to April 2022. The web-based evaluation consisted of 2 think-aloud tasks and a semistructured qualitative interview. Think-aloud tasks assessed efficiency and effectiveness, and the interview investigated satisfaction and overall usability. Verbatim transcripts from the tasks and interviews were analyzed using directed content analysis. RESULTS: Of the 9 participants, all were female and most worked at a local health department (7/9, 78%). A mean of 10.1 (SD 1.4) clicks for task 1 (could be completed in 7 clicks) and 11.4 (SD 2.0) clicks for task 2 (could be completed in 9 clicks) were recorded. For both tasks, most participants required no prompting-89% (n=8) participants for task 1 and 67% (n=6) participants for task 2, respectively. For effectiveness, all participants were able to complete each task accurately and comprehensively. Overall, the participants were highly satisfied with the dashboard with everyone remarking on the utility of using it to support their work, particularly to compare their jurisdiction to others. Finally, half of the participants stated that the ability to share the graphs from the dashboard would be "extremely useful" for their work. The only aspect of the dashboard cited as problematic is the amount of missing data that was present, which was a constraint of the data available about rural jurisdictions. CONCLUSIONS: Think-aloud tasks showed that the SHAREdash allows users to complete tasks efficiently. Overall, participants reported being very satisfied with the dashboard and provided multiple ways they planned to use it to support their work. The main usability issue identified was the lack of available data indicating the importance of addressing the ongoing issues of missing and fragmented public health data, particularly for rural communities.
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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 | 0,029 | 0,036 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 ».