Health literacy and optimizing education materials in a surgical population
Bibliographic record
Abstract
Objective: The aim is to examine and compare the level of health literacy (HL) amongst surgical vascular and abdominal patients and measuring the understandability and actionability of current and optimized education materials.Methods: A cross-sectional design was utilized. Patients undergoing abdominal or vascular surgery, were included for measuring HL with the Newest Vital Sign Dutch (NVS-d) tool. The Dutch version of the Patient Education Materials Assessment Tool (PEMAT) was used to measure the understandability and actionability of current and optimized patient education materials.Results: A total of 101 patients were included, of those 54 (53.5%) have limited HL. Patients with limited HL were significantly older (p < .001), lower educated (p < .001), and had a higher ASA status (p = .005) and Charlson Comorbidity Index score (p < .001). The occurrence of limited HL differed significantly (p = .046) between abdominal versus vascular patients. The understandability varied between 24%-59% and the actionability between 40%-67% of the current education materials. The optimized education materials had a understandability score of 86% and a actionability score of 100%.Conclusions: The high prevalence of inadequate HL emphasizes the importance of nursing and medical staff providing clear information to enable shared decision-making. Besides, it is necessary to evaluate current education materials and optimize these materials according to the level of health literacy to provide health information that is understandable.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".