How Do Publicly Available Allergy-Specific Web-Based Training Programs Conform to the Established Criteria for the Reporting, Methods, and Content of Evidence-Based (Digital) Health Information and Education: Thematic Content Evaluation
Notice bibliographique
Résumé
BACKGROUND: Allergic diseases, such as allergic asthma, rhinitis, and atopic eczema, are widespread, and they are a considerable burden on the health care system. For patients and health care professionals, Web-based training programs may be helpful to foster self-management and provide allergy-specific information, given, for instance, their good accessibility. OBJECTIVE: This study aimed to assess an exploratory sample of publicly available allergy-specific Web-based training programs-that is, interactive, feedback-oriented Web-based training platforms promoting health behavior change and improvement of personal skills-with regard to (1) general characteristics, aims, and target groups and (2) the extent to which these tools meet established criteria for the reporting, methods, and content of evidence-based (digital) health information and education. METHODS: Web-based training programs were identified via an initial Google search and a search of English and German language websites of medical and public health services, such as the European Centre for Allergy Research Foundation (German), Asthma UK, and Anaphylaxis Canada. We developed a checklist from (1) established guidelines for Web-based health information (eg, the Journal of the American Medical Association benchmarks, DISCERN criteria, and Health On the Net code) and (2) a database search of related studies. The checklist contained 44 items covering 11 domains in 3 areas: (1) content (completeness, transparency, and evidence), (2) structure (data safety and qualification of trainers and authors), and (3) impact (effectiveness, user perspective, and integration into health care). We rated the Web-based training programs as completely, partly, or not satisfying each checklist item and calculated overall and domain-specific scores for each Web-based training program using SPSS 23.0 (SPSS Inc). RESULTS: The 15 identified Web-based training programs covered an average of 37% of the items (score 33 out of 88). A total of 7 Web-based training programs covered more than 40% (35/88; maximum: 49%; 43/88). A total of 5 covered 30% (26/88) to 40% (35/88) of all rated items and the rest covered fewer (n=3; lowest score 24%; 21/88). Items relating to intervention (58%; 10/18), content (49%; 9/18), and data safety (60%; 1/2) were more often considered, as opposed to user safety (10%; 0.4/4), qualification of staff (10%; 0.8/8), effectiveness (16%; 0.4/2), and user perspective (45%; 5/12). In addition, in 13 of 15 Web-based training programs, a minimum of 3 domains were not covered at all. Regarding evidence-based content, 46% of all Web-based training programs (7/15) scored on use of scientific research, 53% on regular information update (8/15), and 33% on provision of references (5/15). None of 15 provided details on the quality of references or the strength of evidence. CONCLUSIONS: English and German language allergy-specific Web-based training programs, addressing lay audiences and health care professionals, conform only partly to established criteria for the reporting, methods, and content of evidence-based (digital) health information and education. Particularly, well-conducted studies on their effectiveness are missing.
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 | 0,398 | 0,535 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,006 |
| Bibliométrie | 0,011 | 0,012 |
| Études des sciences et des technologies | 0,003 | 0,005 |
| Communication savante | 0,009 | 0,011 |
| Science ouverte | 0,003 | 0,007 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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; l’étiquette directe de Gemma et le classifieur distillé Codex 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 ».