A144: Resident's Guide to Rheumatology Guide Mobile Application: An International Needs Assessment
Notice bibliographique
Résumé
Background/Purpose: “A Resident's Guide To Pediatric Rheumatology” (the Guide) is a widely accepted resource for pediatric rheumatologists and trainees. In preliminary assessments, uptake of the Guide was broader than intended and it was used by trainees to help with clinical decision‐making, learning and teaching. Users of the Guide suggested that it be developed into a mobile application (app) The Technology Acceptance Model (TAM) provides a framework to assess the perceived usefulness and ease of use of a tool to predict future acceptance and use. Objectives: To determine the International demand amongst pediatric professionals and current trainees for a mobile app format of the Guide. To determine user preferred features, functions, and format to be included in a mobile app using the TAM. Methods: An electronic survey was developed and distributed to pediatric residents at SickKids hospital and to both faculty and trainee members of the international Pediatric Rheumatology list server. The survey included respondent demographics, perceived usefulness, perceived ease of use, and behavioural intention to use the app based on the TAM. Data were analyzed using descriptive statistics. Results: The survey was distributed to 75 pediatric residents and 1132 members of the Pediatric Rheumatology listserver and 135 (12% response rate) completed the survey. The majority of respondents were rheumatologists (53%), while the remainder consisted of Fellows (17%), Pediatric Residents (16%), and other allied health professionals (5%). 93% owned a smartphone and 58% owned a tablet. Most had medically related apps (75%) compared to e‐books (38%), but had similar use for each—Mone to several times per week for 1–15 minutes each time on average. The most useful features of an app would be clinical pictures (e.g. skin rashes), radiology images (e.g. joint x‐rays), and definitions of key terms. Least useful features were games and multiple‐choice questions. Additional features included a searchable index and links to journal articles. Looking at the TAM, the vast majority of respondents thought that the mobile app would enhance trainees' learning and teaching effectiveness. Greater than 80% of respondents consistently supported its perceived ease of use. 55% stated that they were likely to use the app often. 86% felt it was important for the app to be developed. If the app was not available for free, a majority (43%) of respondents were willing to pay for the app with a most willing to pay up to $5.00, and 10% willing to pay up to $10 for access to the app. Conclusion: Development of the Guide app was well supported with adding features such as clinical photographs, radiology images, definitions and searchable index. TAM showed the intention to use the app in the future will be most determined by the perceived ease of use which was consistently high in the survey. Interestingly, users were willing to pay for the app if it was not free. Future steps include a qualitative study ultilizing focus groups to assess the perceived functionality, usability, facilitators and barriers in using the Guide app prototype to create the most targeted, user friendly app.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».