An Educational Needs Assessment for Patients with Liver Disease
Bibliographic record
Abstract
INTRODUCTION: Liver disease forms a global health burden and is a cause of significant morbidity and mortality. Good patient education has proven to be a key tool in disease management, providing significant benefit in knowledge and behavioral modifications. To develop effective educational tools, a good understanding of patient educational needs and preferred learning methods is necessary. Few studies have evaluated the educational needs of patients with liver disease. This study aims to assess the educational needs of patients at a large tertiary liver center. METHOD: This study was a questionnaire-based cross-sectional study evaluating patient demographics,perceived and unperceived educational needs (hepatitis B and hepatitis Cknowledge) at a tertiary liver centre. RESULTS: A total of 300 patients completed the questionnaire. Most patients stated they were "extremely" or "quite" interested in learning more about their liver condition (84.9%, n=242), in either "moderate" or "a lot of" detail (94.6%, n=202). There was no association between gender, age, level of education, annual income and interest of patients in learning more about their liver condition. There was a significant association between number of clinic visits and interest to learn more (p=0.022), but there was no association between the duration of their follow-up at the clinic and their interest to learn more (p=0.243). CONCLUSIONS: Overall, patients showed great interest in learning more about their liver condition, potentially indicating a need for more educational programs. Most patients prefer reading (via internet or pamphlets/brochures) or one-to-one discussions, giving us a good sense of potentially successful educational strategies that will fit the needs of most patients.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".