The relationship between electronic health literacy and individual factors among adults with chronic pain: A cross-sectional study
Bibliographic record
Abstract
Introduction: Chronic pain requires individuals to develop self-management skills that rely on health literacy and, more recently, eHealth literacy. Very few studies have investigated potential predictors of eHealth literacy in chronic pain patients. Therefore, the purpose of this study is to explore potential predictors of eHealth literacy among individual characteristics and pain-related clinical factors, as a preliminary step to understanding the multi-variable relationships that could be examined in a larger study. Methods: A cross-sectional online survey was distributed to adults living in the United States with various chronic pain conditions using Amazon’s Mechanical Turk. A convenience sample of 196 participants was recruited. The independent variables of interest regarding their relationship with eHealth literacy (dependent variable) included demographics, health literacy, chronic pain severity, pain attitudes and coping skills. Chi square tests of association, and independent samples t-tests were used to examine the bivariate relationships. Results: The majority of the sample suffered from chronic pain for more than 2 years with 48% suffering from chronic back pain. Most of the sample (n=184, 93.9%) had high eHealth literacy. Significant relationships were found between eHealth literacy and the following variables: marital status, education level, and age, as well as health literacy, chronic pain interference with activities and chronic pain attitudes. These warrant further exploration in a larger study using logistic regression. Conclusions: our findings provide new information on the relationship between eHealth literacy levels, pain-related individual factors such as attitudes toward pain, and clinical outcomes, i.e., pain interference with physical and psychological function. Although further research is needed to investigate eHealth literacy predictors and mediators, these findings promote the evidence-based development and evaluation of interventions enhancing eHealth literacy skills, as well as self-management skills of chronic pain patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".