866
Views
34
CrossRef citations to date
0
Altmetric
Articles

Predictors of Self and Surrogate Online Health Information Seeking in Family Caregivers to Cancer Survivors

Pages 939-953 | Received 21 May 2015, Accepted 04 Jul 2015, Published online: 14 Dec 2015
 

Abstract

The purpose of this research is to investigate various factors predicting online health information seeking for themselves (self OHIS) and online health information seeking for others (surrogate OHIS) in family caregivers to cancer survivors. To address this purpose, this study applies the comprehensive model of information seeking as a theoretical framework for explaining the relationships between various predictors and two types of OHIS. The data used in this study were taken from the Health Information National Trends Survey 4. A total of 1,113 family caregivers were included in this study. Logistic regression analyses were conducted to examine the effects of predictors on Internet use for health information seeking. Caregivers’ self and surrogate OHIS were commonly predicted by their self-rated health and attention to the Internet. However, age, race, and education were significantly associated with self OHIS only, while gender and marital status were significantly associated with surrogate OHIS only. These results suggest that family caregivers’ self and surrogate OHIS are predicted by common factors, as well as predicted by different specific factors.

View correction statement:
Erratum

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 53.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 317.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.