Exploring Young Adults' Experiences and Beliefs in Asthma Medication Management: Pilot Qualitative Study Comparing Human and Multiple AI Thematic Analysis
Notice bibliographique
Résumé
Background: Young adults take their asthma maintenance medication 67% of the time or less. Understanding the specific needs and behaviors of young adults with asthma is essential for developing targeted interventions to improve disease self-management. Artificial intelligence (AI) has demonstrated its utility in summarizing and identifying patterns in qualitative research and may support or augment human coding efforts. However, there is pause literature to support this assertion. Objective: The objective of this study is to begin to explore the medication management-related needs of young adults with asthma via a pilot feasibility study. We aim to understand how to best assist young adults with asthma self-management and to identify potential areas where digital health interventions can provide support. We further aimed to understand the comparative outcome of human versus multiple AI platforms in performing thematic analysis. Methods: This study purposefully sampled young adults between the ages of 18 years and 29 years who had a prescription for an inhaled corticosteroid (ICS) and were either students or staff of a large metropolitan university in the northeastern United States. Semistructured interviews lasting 40 minutes on average were conducted with 4 participants via a teleconferencing application to elicit young adults' opinions on the topic. Interviews were recorded and transcribed verbatim using Otter.ai (Otter.ai, Inc). Investigators listened to the recording to confirm the accuracy of transcriptions and to make corrections when necessary. After performing a second round of line-by-line coding, the codes were reviewed by investigators and grouped into broader, overarching themes. All investigators reviewed and discussed the final codes. Human qualitative data analyses were performed using NVivo 14 software (QSR International). After completing human analyses, the investigators performed thematic analysis with multiple AI platforms (Google Gemini, Microsoft Copilot, and OpenAI's ChatGPT) to compare the final themes with investigator-derived themes. Results: Human analysis yielded 4 themes: support from clinicians, social support, digital self-management support, and educational support. The AI-based analysis also generated similar themes with different labels. The level of overlap on the underlying concept between humans, Gemini, Copilot, and ChatGPT was high, accounting for the fact that, although the specific labels differed, they referred to the same concept. Conclusions: Findings from our pilot exploratory study offer insights into the necessity for a holistic approach in supporting young adults with asthma. Based on the health belief model, if the identified multifaceted needs are addressed, health care systems may support medication adherence and improve health outcomes for this understudied patient population. Our pilot study also offers preliminary findings that artificial intelligence may be leveraged for successful thematic analysis of qualitative data with appropriate caution.
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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 | 0,019 | 0,017 |
| 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 | 0,006 | 0,005 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 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 ».