Quality Awareness and Its Influence on the Evaluation of App Meta-Information by Physicians: Validation Study
Notice bibliographique
Résumé
BACKGROUND: Meta-information provided about health apps on app stores is often the only readily available source of quality-related information before installation. OBJECTIVE: The purpose of this study was to assess whether physicians deem a predefined set of quality principles as relevant for health apps; whether they are able to identify corresponding information in a given sample of app descriptions; and whether, and how, this facilitates their informed usage decisions. METHODS: All members of the German Society for Internal Medicine were invited by email to participate in an anonymous online survey over a 6-week period. Participants were randomly assigned one app description focusing on cardiology or pulmonology. In the survey, participants were asked three times about whether the assigned description sufficed for a usage decision: they were asked (1) after giving an appraisal of the relevance of nine predefined app quality principles, (2) after determining whether the descriptions covered the quality principles, and (3) after they assessed the availability of detailed quality information by means of 25 additional key questions. Tests for significance of changes in their decisions between assessments 1 and 2, and between assessments 2 and 3, were conducted with the McNemar-Bowker test of symmetry. The effect size represents the discordant proportion ratio sum as a quotient of the test statistics of the Bowker test and the number of observation units. The significance level was set to alpha=.05 with a power of 1-beta=.95. RESULTS: A total of 441 of 724 participants (60.9%) who started the survey fully completed the questionnaires and were included in the evaluation. The participants predominantly rated the specified nine quality principles as important for their decision (approximately 80%-99% of ratings). However, apart from the practicality criterion, information provided in the app descriptions was lacking for both groups (approximately 51%-92%). Reassessment of the apps led to more critical assessments among both groups. After having familiarized themselves with the nine quality principles, approximately one-third of the participants (group A: 63/220, 28.6%; group B: 62/221, 28.1%) came to more critical usage decisions in a statistically significant manner (McNemar-Bowker test, groups A and B: P<.001). After a subsequent reassessment with 25 key questions, critical appraisals further increased, although not in a statistically significant manner (McNemar-Bowker, group A: P=.13; group B: P=.05). CONCLUSIONS: Sensitizing physicians to the topic of quality principles via questions about attitudes toward established quality principles, and letting them apply these principles to app descriptions, lead to more critical appraisals of the sufficiency of the information they provided. Even working with only nine generic criteria was sufficient to bring about the majority of decision changes. This may lay the foundation for aiding physicians in their app-related decision processes, without unduly taking up their valuable time.
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 | 0,292 | 0,655 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,008 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,003 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».