Relationship Between Health Literacy (HL), eHealth Literacy (eHL), Subjective Well-Being (SWB) and COVID-19 Related Health Behaviours Among Canadian University Students: A Cross-Sectional Study
Bibliographic record
Abstract
Background: One significant predictor of health practices and outcomes is health literacy. Health literacy is an individual's ability to search, understand, appraise, and apply health information. Much research has occurred on health literacy (HL), ehealth literacy (eHL), subjective well-being (SWB), and COVID-19 health-related behaviours; however, few studies have explored the association between these variables, particularly within Canada. Understanding university students' HL and eHL levels would yield beneficial information on a group that impacts their community, particularly in a pandemic setting where students gather in large groups. Methods: An online survey was administered to 336 university students via Google Forms from April 2022 - December 2022. The survey used the Computer Adaptation of Newest Vital Sign to measure HL, eHEALS to measure eHL, a modified COVID-19-Related Health Behaviors questionnaire, and the Satisfaction with Life Scale to measure SWB. Data analysis was analyzed using Pearson correlation and hierarchical regression analysis on SPSS. Results: Most participants were, on average, 26.1 years and 76.7% identified as female. The statistical analysis revealed that eHL significantly predicted COVID-19-related health behaviours, whereas SWB and HL were not statistically significant predictors of COVID-19-related health behaviours. Overall, the results suggest that individuals with higher eHL levels may engage in less healthy behaviours related to COVID-19. Conclusion: Although this study found that eHL predicted COVID-19 related health behaviours, HL and SWB did not seem to have a significant relationship with COVID-19 related health behaviours. This suggests that other interdisciplinary factors are involved in understanding the relationship between HL, eHL, SWB, and COVID-19 health-related behaviours among Canadian university students. Cultural beliefs and values, political alignment, fear and anxiety, misinformation, and disinformation are reasons HL may not be a strong predictor of COVID-19 related health behaviours. Future research should continue to explore a more interdisciplinary approach to public health practice geared towards researching the factors that affect health behaviours on college campuses will improve future health and well-being outcomes among this population.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".