Identifying the Information Needs and Format Preferences for Web-Based Content Among Adults With or Parents of Children With Attention-Deficit/Hyperactivity Disorder: Three-Stage Qualitative Analysis
Notice bibliographique
Résumé
BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent childhood and adult behavioral disorder. Internet searches for ADHD information are rising, particularly for diagnosis and treatment. Despite effective ADHD treatments, research suggests that there are delays in seeking help for ADHD. Identifying ways to shorten delays is important for minimizing morbidity associated with ADHD. One way to shorten these delays is to improve internet health information resources. Research shows that parents of children with ADHD feel that much of the information available is technical and not tailored for their child's needs and verbal instructions given by health care providers were too pharmacologically focused with limited information about how to manage and support ADHD symptoms in daily living. A majority of parents search the internet for general and pharmacological information for ADHD and prefer web-based resources for learning about ADHD, but web-based resources may be inaccurate and of low quality. Ensuring accurate information through the internet is an important step in assisting parents and adults in making informed decisions about the diagnosis and treatment of ADHD. OBJECTIVE: Although a great deal of information regarding ADHD is available on the internet, some information is not based on scientific evidence or is difficult for stakeholders to understand. Determining gaps in access to accurate ADHD information and stakeholder interest in the type of information desired is important in improving patient engagement with the health care system, but minimal research addresses these needs. This study aims to determine the information needs and formatting needs of web-based content for adults with ADHD and parents of children with ADHD in order to improve user experience and engagement. METHODS: This was a 3-phase study consisting of in-depth phone interviews about experiences with ADHD and barriers searching for ADHD-related information, focus groups where participants were instructed to consider the pathways by which they made decisions using web-based resources, and observing participants interacting with a newly developed website tailored for adults with potential ADHD and caregivers of children who had or might have ADHD. Phase 1 individual interviews and phase 2 focus groups identified the needs of the ADHD stakeholders related to website content and format. Interview and focus group findings were used to develop a website. Phase 3 used think-aloud interviews to evaluate website usability to inform the tailoring of the website based on user feedback. RESULTS: Interviews and focus group findings revealed preferences for ADHD website information and content, website layout, and information sources. Themes included a preference for destigmatizing information about ADHD, information specific to patient demographics, and evidence-based information tailored to lay audiences. CONCLUSIONS: ADHD stakeholders are specifically seeking positive information about ADHD presented in a user-friendly format.
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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 | 0,028 | 0,049 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,005 | 0,004 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,001 | 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 ».