Exploring Influencing Factors on Health Literacy Knowledge and Experience Among Healthcare Providers: A Cross-Sectional Study
Bibliographic record
Abstract
Fadi Khraim,1 Rami A Elshatarat,2 Carolyn Wolsey,3 Jessie Johnson,4 Dina Schnurman,5 Lisa Thornton,5 Fadwa Alhalaiqa,1 Mohammed Al-Hassan,6 Amal Al-Farsi5 1College of Nursing, Qatar University, Doha, Qatar; 2College of Nursing, Taibah University, Madinah, Saudi Arabia; 3School of Nursing, University of Tasmania, Hobart, Tasmania, Australia; 4Faculty of Nursing, Beal University, Sackville, New Brunswick, Canada; 5Department of Pediatric Rehabilitation, Sidra Medicine, Doha, Qatar; 6Tamayuz Simulation Center, QU Health Sector, Qatar University, Doha, QatarCorrespondence: Fadi Khraim, College of Nursing, Qatar University, PO Box: 2713, Doha, Qatar, Email fkhraim@qu.edu.qaIntroduction: Health literacy is a critical factor in improving healthcare outcomes and patient self-care practices. Healthcare providers play a vital role in supporting patients with diverse health literacy levels.Objective: This cross-sectional study aims to explore the factors influencing health literacy knowledge and experience among healthcare providers in Qatar.Methods: A structured questionnaire, including sociodemographic and work-related data, was administered to a diverse sample of healthcare providers, including physicians, nurses, and other allied health practitioners from a single institution in Qatar. The Health Literacy Knowledge and Experience Scale-2 was used to evaluate participants’ health literacy knowledge and experience. The study employed a web-based survey and involved 121 healthcare providers. Descriptive statistics, t-tests, and analysis of variance were utilized for data analysis.Results: The study revealed that age and profession significantly influenced health literacy knowledge among healthcare providers. Healthcare providers in the age group 20– 39 years had higher health literacy knowledge scores. In contrast, nationality/race significantly affected health literacy experience, with Arabic participants reporting higher scores of health literacy experience. The study also found that healthcare providers who had taken formal courses related to health literacy demonstrated higher health literacy experience scores.Conclusion: This study contributes to the understanding of factors influencing health literacy knowledge and experience among healthcare providers in Qatar. The findings emphasize the importance of addressing age, nationality/race, and formal training in health literacy to enhance the capacity of healthcare providers in supporting patients with varying health literacy levels. Healthcare policies should aim to integrate formal health literacy training among healthcare providers in order to enhance healthcare delivery and improve patient self-care practices.Keywords: health literacy, health literacy knowledge, health literacy experience, healthcare providers, Qatar
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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 | 0.000 |
| 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) | 0.002 | 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 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".