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 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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".