Genomic Health Literacy Interventions in Pediatrics: Scoping Review
Notice bibliographique
Résumé
BACKGROUND: The emergence of genetic and genomic sequencing approaches for pediatric patients has raised questions about the genomic health literacy levels, attitudes toward receiving genomic information, and use of this information to inform treatment decisions by pediatric patients and their parents. However, the methods to educate pediatric patients and their parents about genomic concepts through digital health interventions have not been well-established. OBJECTIVE: The primary objective of this scoping review is to investigate the current levels of genomic health literacy and the attitudes toward receiving genomic information among pediatric patients and their parents. The secondary aim is to investigate patient education interventions that aim to measure and increase genomic health literacy among pediatric patients and their parents. The findings from this review will be used to inform future digital health interventions for patient education. METHODS: A scoping review using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines and protocols was completed using the following databases: MEDLINE, Embase, CINAHL, and Scopus. Our search strategy included genomic information inclusive of all genetic and genomic terms, pediatrics, and patient education. Inclusion criteria included the following: the study included genetic, genomic, or a combination of genetic and genomic information; the study population was pediatric (children and adolescents <18 years) and parents of patients with pediatric illnesses or only parents of patients with pediatric illnesses; the study included an assessment of the knowledge, attitudes, and intervention regarding genomic information; the study was conducted in the last 12 years between 2008 and 2020; and the study was in the English language. Descriptive data regarding study design, methodology, disease population, and key findings were extracted. All the findings were collated, categorized, and reported thematically. RESULTS: Of the 4618 studies, 14 studies (n=6, 43% qualitative, n=6, 43% mixed methods, and n=2, 14% quantitative) were included. Key findings were based on the following 6 themes: knowledge of genomic concepts, use of the internet and social media for genomic information, use of genomic information for decision-making, hopes and attitudes toward receiving genomic information, experiences with genetic counseling, and interventions to improve genomic knowledge. CONCLUSIONS: This review identified that older age is related to the capacity of understanding genomic concepts, increased genomic health literacy levels, and the perceived ability to participate in decision-making related to genomic information. In addition, internet-searching plays a major role in obtaining genomic information and filling gaps in communication with health care providers. However, little is known about the capacity of pediatric patients and their parents to understand genomic information and make informed decisions based on the genomic information obtained. More research is required to inform digital health interventions and to leverage the leading best practices to educate these genomic concepts.
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 | 0,018 | 0,108 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,006 | 0,007 |
| Bibliométrie | 0,012 | 0,012 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,004 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 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 ».