Perceived eHealth literacy and health literacy among people with spinal cord injury: A cross-sectional study
Bibliographic record
Abstract
OBJECTIVES: This purpose of this research was to (1) to evaluate eHealth and general health literacy levels among individuals with spinal cord injury (SCI) and (2) to identify relationships between eHealth literacy, general health literacy, and various sociodemographic factors. DESIGN: Cross-sectional. SETTING: The study was conducted in the community setting. PARTICIPANTS: As part of a larger study, a total of 50 community-dwelling individuals with SCI were recruited. INTERVENTIONS: n/a. OUTCOME MEASURES: Quantitative online survey data were collected on participants' sociodemographic characteristics, eHealth literacy (using the eHealth Literacy Scale), general health literacy (using the Brief Health Literacy Screening Tool). RESULTS: The average age of participants was 49 years old; 25 participants were male and 25 were female. A total of 39 participants experienced traumatic SCI and 11 participants experienced non-traumatic SCI. Participants demonstrated moderate levels of eHealth literacy (31.6 out of 40) and general health literacy (17.6 out of 20). A significant, positive correlation was found between eHealth literacy and general health literacy. Significant, positive correlations were found between general health literacy and sociodemographic factors, including income and education. A significant, negative correlation was found between general health literacy and time since injury. CONCLUSION: No previous studies we are aware of have evaluated perceived eHealth literacy and general health literacy among people with SCI. This study demonstrated the diverse range of eHealth literacy levels in SCI populations and how this, and other factors, may impact an individual's ability to self-manage and adopt to eHealth technologies.
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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 | 0.011 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".