Educational Needs and Interests of Patients With Liver Disease: A Systematic Review
Bibliographic record
Abstract
Introduction: Liver disease forms a global health burden and is a cause of significant morbidity and mortality. Good patient education is a key tool in disease management, providing significant benefit in knowledge and behavioral modifications. This study aims to evaluate the quality and results of studies assessing the perceived and unperceived educational needs of liver disease patients. Methods: Multiple databases were searched for studies assessing educational needs of individuals with any type of liver disease. The initial search identified 584 publications. Inclusion criteria: 1. Study population of patients with any form of liver disease. 2. Studies including an assessment of patient knowledge about their or any other form of liver disease, or of patient interest in educational programs. Final analysis included 40 studies. Data extracted included methodology and results of perceived and unperceived educational needs of patients. The quality of each study was appraised. To simplify the analysis, the knowledge domains that were assessed in the studies were grouped into 6 categories: diagnosis, symptoms, complications, modes of transmission/prevention, treatment/self-care, and other (eg etiology, prevalence). All studies had their own limitations, and each study assessed patients in different settings, so a cut-off score was used to determine high-quality studies to be reviewed in detail. The 7 studies with the highest scores are discussed in detail, all of which assessed 2 or more domains of knowledge. Results: Of the 40 studies that were evaluated in this review, 85% (n= 34) included patients with hepatitis B or C virus. Among these studies, there was a wide variation in modes of assessment and target populations. Although the heterogeneity of studies make it difficult to get an objective sense of where knowledge gaps lie, we did identify some common themes. Knowledge gaps were present in all domains, including very relevant ones such as disease diagnosis. Conclusion: Overall, it was found that there are large knowledge gaps that should be addressed in all liver disease patients, as it has been shown that patient awareness does play a large role in improving behavioural outcomes. Another common theme was the variation in knowledge based on the level of patient health literacy. Further studies should aim to create validated questionnaires to assess patients' educational needs. This will allow the future development of effective and more targeted educational resources.
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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 | 0.011 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".