Reducing Negative Outcomes of Online Consumer Health Information: Qualitative Interpretive Study with Clinicians, Librarians, and Consumers
Notice bibliographique
Résumé
BACKGROUND: There has been an exponential increase in the general population's usage of the internet and of information accessibility; the current demand for online consumer health information (OCHI) is unprecedented. There are multiple studies on internet access and usage, quality of information, and information needs. However, few studies explored negative outcomes of OCHI in detail or from different perspectives, and none examined how these negative outcomes could be reduced. OBJECTIVE: The aim of this study was to describe negative outcomes associated with OCHI use in primary care and identify potential preventive strategies from consumers', health practitioners', and health librarians' perspectives. METHODS: This included a two-stage interpretive qualitative study. In the first stage, we recruited through a social media survey, a purposeful sample of 19 OCHI users who had experienced negative outcomes associated with OCHI. We conducted semistructured interviews and performed a deductive-inductive thematic analysis. The results also informed the creation of vignettes that were used in the next stage. In the second stage, we interviewed a convenient sample of 10 key informants: 7 health practitioners (3 family physicians, 2 nurses, and 2 pharmacists) and 3 health librarians. With the support of the vignettes, we asked participants to elaborate on (1) their experience with patients who have used OCHI and experienced negative outcomes and (2) what strategies they suggest to reduce these outcomes. We performed a deductive-inductive thematic analysis. RESULTS: We found that negative outcomes of OCHI may occur at three levels: internal (such as increased worrying), interpersonal (such as a tension in the patient-clinician relationship), and service-related (such as postponing a clinical encounter). Participants also proposed three types of strategies to reduce the occurrence of these negative outcomes, namely, providing consumers with reliable OCHI, educating consumers on how to assess OCHI websites, and helping consumers present and discuss the OCHI they find with a health professional in their social network or a librarian for instance. CONCLUSIONS: We examined negative outcomes associated with using OCHI from five complementary perspectives (consumers, family physicians, pharmacists, nurses, and health librarians). We identified a construct of OCHI use-related tension that included and framed all negative outcomes. This construct has three dimensions (three interdependent levels): internal, interpersonal, and service-related tensions. Future research can focus on the implementation and effectiveness of the proposed strategies, which might contribute to reducing these tensions.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,031 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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; les deux têtes enseignantes 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 ».