Electronic health literacy among adults with chronic pain: A descriptive, cross-sectional survey
Bibliographic record
Abstract
Background: Approximately 100 million American adults are living with chronic pain, which costs the healthcare system an average of $560–635 billion each year. Levels of health literacy and ehealth literacy are important factors in determining a patient’s capacity to manage pain and the multidimensional impact of pain. To our knowledge, few studies have specifically examined the level of ehealth literacy and its association with health literacy among chronic pain patients. The purpose of this study was to 1) assess the levels of health literacy and ehealth literacy in adults with chronic pain, and 2) examine the relationship between health literacy and ehealth literacy skills among adults diagnosed and living with chronic pain. Methods: A non-experimental, descriptive cross-sectional survey was distributed to adults with chronic pain. A total of 196 participants were asked to complete questionnaires related to demographic characteristics, ehealth literacy (eHEALS), and health literacy (HLQ). Descriptive statistics were calculated to summarize data from all the scales used in the study. Results: The average level of ehealth literacy was 32.6 (SD 4.4) out of 40. The level of health literacy was measured by four subscales: having sufficient information to manage my health (mean=2.8; SD=0.55), appraisal of health information (mean=3.27; SD=0.41), ability to find good health information (mean=3.68; SD=0.45), and understanding health information well enough to know what to do (mean=3.66; SD=0.48). Two subscales (i.e., appraisal of health information, ability to find good health information) were significant in predicting ehealth literacy total score. Discussion and Conclusions: Examining ehealth literacy and health literacy can assist in the dissemination of accessible and understandable chronic-pain-related health information for individuals of all health literacy levels. In addition, this will allow the development of interventions for enhancing ehealth literacy skills and/or usability of web-based information for adults with chronic pain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 0.001 |
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".