Health and eHealth Literacy of Patients With Diabetes in Low-Income Countries: Perspective From Guinea and Burkina Faso
Bibliographic record
Abstract
Background: Diabetes is a significant health concern in sub-Saharan Africa, emphasizing the importance of assessing the health literacy and eHealth skills of hospitalized patients with diabetes. This study evaluated the health literacy and eHealth literacy of patients with diabetes at Donka Hospital in Guinea and Sanou Sourou Hospital in Burkina Faso, providing insights for targeted interventions and mobile health (mHealth) solutions to improve self-management and treatment outcomes. Objective: The aim of this study is to evaluate the levels of health literacy and eHealth literacy among patients at Sanou Sourou Hospital in Burkina Faso and Donka Hospital in Guinea. Methods: The study included 45 participants from Donka Hospital and 47 from Sanou Sourou Hospital. Data collection took place in May 2022, focusing on variables such as gender, age, education, income, and technology access. Health literacy and eHealth literacy were measured using the Brief Health Literacy Screen (BHLS) and the eHealth Literacy Scale (eHEALS), respectively. Statistical analysis was performed using SPSS 28. Results: The results indicated that 64% (64/99) of participants at Donka Hospital and 57% (57/99) at Sanou Sourou Hospital were female. The majority of participants (48/98, 49% in both hospitals) fell within the age range of 25-50 years. High rates of illiteracy were observed (62/100, 62% in Donka Hospital and 59/100, 59% in Sanou Sourou Hospital). Smartphone ownership was prevalent (62/99, 62% at Donka Hospital and 64/100, 64% at Sanou Sourou Hospital). Participants reported occasional use of technology for basic purposes and frequent internet usage for accessing health information. However, a significant proportion of participants demonstrated low health literacy (73/99, 73% at Donka Hospital; 79/101, 78% at Sanou Sourou Hospital) and inadequate eHealth literacy (57/100, 57% at Donka Hospital; 62/100, 62% at Sanou Sourou Hospital). Education was observed to positively correlate with health literacy, while age displayed a moderate negative correlation. Weak correlations were observed between gender, income, and health literacy, but these were not statistically significant. No significant correlation was found between the scores of the BHLS and the eHEALS in either hospital. Conclusions: This study highlights the importance of targeted educational interventions and mHealth solutions aimed at enhancing health and eHealth literacy among hospitalized patients with diabetes. Addressing both health literacy and eHealth literacy is paramount for improving diabetes management and treatment outcomes in Guinea and Burkina Faso. Targeted interventions and mHealth solutions have the potential to empower patients, enabling their active involvement in health care decisions and ultimately improving overall health outcomes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".