Health Literacy Applications in Patients With Cancer
Bibliographic record
Abstract
BACKGROUND: Only 12% of Americans have adequate health literacy (HL) skills; 36% have literacy skills at a basic or below-basic level. Increased HL can assist patients with cancer to make intentional and informed healthcare decisions. OBJECTIVES: This article provides an overview of HL in patients with cancer. Risk factors, assessment, and strategies to communicate with patients and families who have limited HL skills are essential in providing holistic care. Considerations in addressing HL are also given for telehealth nurses. METHODS: The authors searched PubMed®, CINAHL®, Google Scholar™, and reputable websites for literature published from 2006 to 2024 about HL in patients with cancer. Some of the search terms were related to risk factors, HL assessment, and interventions to address low HL. FINDINGS: Risks for low HL included lower education level and socioeconomic status, as well as non-White race, but an individualized assessment is important in identifying patients with low HL. Educating and communicating with patients and families with low HL in all settings, such as inpatient, ambulatory, and telehealth, include using a teach-back method to ensure understanding and application of information, using written materials at an appropriate reading level (fifth grade or less), and reinforcing teaching at consecutive visits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".