Individuals with Chronic Conditions Want More Guidance from Health Professionals in Finding Quality Online Health Sources
Notice bibliographique
Résumé
Objective – To explore how and when individuals with chronic health conditions seek out health information online, and the challenges they encounter when doing so. Design – Qualitative study employing thematic analysis. Setting – Urban Western Australia. Subjects – 17 men and women between 19 and 85 years of age with at least 1 chronic health condition. Methods – Participants were recruited in late 2013 at nine local pharmacies, through local radio, media channels, and a university's social media channels. Participants were adult English speakers who had looked for information on their chronic health condition(s) using the Internet. Semi-structured face-to-face interviews were conducted with each participant, audio recorded, and transcribed. The transcripts were coded in QSR Nvivo using two different processes – an initial data-driven inductive approach to coding, followed by a theory driven analysis of the data. Main Results – Three major themes emerged: trust, patient activation, and relevance. Many of the participants expressed trust both in health professionals and in the efficacy of search engines like Google. However, there was uncertainty about the quality of some of the health information sources found. Searching for information online was seen by some participants as a way to feel more empowered about their condition(s) and treatment, but they reported frustration in finding information that was relevant to their specific condition(s) given the volume of information available. Low health literacy emerged in participant interviews as an intrinsic barrier to effective online searches for health information, along with low patient motivation and lack of time. The many extrinsic barriers identified included difficulty determining the quality of information found, the accessibility of the information (e.g., journal paywalls), and poor relationships with health care providers. Conclusion – Individuals look for online health information to help manage their chronic illnesses, but their ability to do so is influenced by their levels of health literacy and other external barriers to effective online navigation. Consumers may prefer to receive recommendations from health professionals for high quality health websites rather than training in how to navigate and identify these resources themselves.
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 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 | 0,006 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,185 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 tête enseignante, 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 ».