Web-Based Health Information–Seeking Methods and Time Since Provider Engagement: Cross-sectional Study
Notice bibliographique
Résumé
BACKGROUND: The use of web-based methods to seek health information is increasing in popularity. As web-based health information (WHI)-seeking affects health-related decision support and chronic symptom self-management, WHI-seeking from online sources may impact health care decisions and outcomes, including care-seeking decisions. Patients who are routinely connected to physicians are more likely to receive better and more consistent care. Little is known about whether WHI-seeking impacts the frequency at which patients engage with health care providers. OBJECTIVE: Our primary objective was to describe the associations between the use of web-based methods to seek information about one's own health and the time since last engaging with a health care provider about one's own health. Additionally, we aimed to assess participants' trust in health care organizations to contextualize our findings. METHODS: We analyzed data from US adults participating in the nationally representative Tufts Equity in Health, Wealth, and Civic Engagement Survey (N=1034). Bivariate associations between demographic characteristics and health information-seeking methods were assessed with Pearson chi-squared tests. Bivariate associations of Medical Mistrust Index (MMI) scores with each health information-seeking method and time since provider engagement were assessed with F tests and adjusted Wald tests. We fit a multivariable logistic regression model to assess the association between WHI-seeking within the 12 months prior to survey (alone or in combination with provider-based methods versus provider only) and engagement with a provider more than 1 year prior to the time of survey, adjusting for age, race and ethnicity, sex, education, insurance coverage, and MMI. RESULTS: Age, race and ethnicity, educational attainment, health insurance source, MMI, and time since provider engagement were each significantly associated with the health information-seeking method in bivariate analyses. Compared to using only provider-based health information seeking methods, WHI-based methods alone or in combination with provider-based methods were associated with a 51% lower likelihood (odds ratio 0.49, 95% CI 0.27-0.87) of engaging with a provider within the previous year. Participants who used WHI-seeking methods alone and those who had not engaged with a health care provider within the previous year demonstrated a higher mean MMI score; however, MMI was not a significant predictor of time since engagement with a provider in the multivariable analysis. CONCLUSIONS: Our findings from a nationally representative survey suggest that for those who use WHI-seeking methods (alone or in combination with provider-based information-seeking methods), there is a statistically significant lower likelihood of engaging with a provider in a year compared to those who only use provider-based methods. Future research should consider the intent of a person's visit with a provider, trust in health care systems, methods of provider engagement, and specific web-based platforms for health information.
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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 | 0,003 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».