Age and Context Sensitivity Associated with Reduced Success in Finding Health Information Online
Bibliographic record
Abstract
A Review of: Agree, E. M., King, A. C., Castro, C. M., Wiley, A., & Borzekowski, D. L. G. (2015). “It’s got to be on this page”: Age and cognitive style in a study of online health information seeking. Journal of Medical Internet Research, 17(3), e79. http://dx.doi.org/10.2196/jmir.3352 Objective – To determine the extent to which age and cognitive style influence an individual’s ability to successfully locate health information online. Design – Quantitative study employing scales and regression analysis. Setting – A school of public health and a school of medicine at two universities in the United States of America. Subjects – 346 men and women 35 years or older. Methods – Participants for the Online Health Study (OHS) were recruited from the community at both study sites using stratified sampling and screened with a web-based tool to ensure they had the necessary level of digital literacy to complete the study tasks. Once enrolled, participants completed the Rapid Estimate of Adult Literacy in Medicine (REALM) to measure their health literacy and the Witkin Group Embedded Figures Test (GEFT) to determine their cognitive style (labelled by researchers as context sensitive or context independent). Participants were asked to search online for answers to six specific questions on heart-healthy diets, flu vaccinations, alternative medicine and memory, genetic testing, assistive medical technology, and skin cancer, with 15 minutes of search time allowed for each question. Participants reported their answers after each search, which were later assigned scores for accuracy and for specificity. When combined, these two scores were used as a measure of success. Researchers used STATA 11 statistical software to run logistic regression, ordinal logistic regression, and generalized linear models on the data in order to predict which variables were associated with success on the search tasks. Main Results – Only 323 of the 346 participants completed all study tasks, and their data formed the basis of the analysis. On average, participants correctly answered 4.1 out of 6 questions. Participants provided the most accurate and successful answers for the question on heart-healthy foods, and the least accurate answers on the question about seasonal flu shots. They were the least successful in answering the question about herbal supplements for memory. Across all models, older participants were less likely to be successful in locating the answers to the questions than younger participants, even controlling for the other variables measured in the study. In particular, older participants had the most difficulty with the question on medical technology, which required the use of mapping. Overall, the models suggest that higher levels of education, greater daily Internet use, and higher health literacy were associated with greater success on the search tasks, the extent to which varied from question to question. The exception in the case of education was the question relating to herbal supplements and memory, as participants with higher levels of education were more likely to score poorly in their responses. Participants whose cognitive style was found to be context sensitive were less likely to find the information needed in their online searches than those who were context independent, particularly on the questions relating to a heart-healthy diet, skin cancer, and medical technology. Conclusion – The study suggests that age, cognitive style, level of health literacy, daily Internet use, and prior education are all important variables in determining whether an individual can successfully take advantage of the increasing amount of health information available on the Internet. Specific approaches to web design could be used to improve the success rate of those who are context sensitive, and greater support and direction to reputable online health sources from medical and information professionals could assist those who are less health literate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.358 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".