Effective interventions to improve the health literacy of cancer patients
Bibliographic record
Abstract
Health literacy (HL) refers to the cognitive and social abilities that are determinants in the motivation and capacity of the individual to access, understand and use information for the care of one's own health. In oncology, increased survival, navigation of the healthcare system, the many different forms of treatment and the management of adverse effects/outcomes make HL a critical factor in patient care. The objective of this study is to identify the structure, content and effectiveness of interventions to improve HL in cancer patients. MATERIALS AND METHODS: A literature review was performed using the '(health literacy OR Cancer Literacy) AND Cancer AND Intervention' strategy on seven multidisciplinary databases. Studies that intervened in subjects diagnosed with cancer and treating HL explicitly as a variable to be measured were included. RESULTS: One thousand two hundred and thirty-six abstracts were retrieved. Eight studies met the inclusion criteria. Research focused on patients diagnosed with breast cancer or prostate cancer. Interventions used multimedia resources and face-to-face interactions. No study defined HL. HL was usually a secondary outcome. There is high variability in the design of studies and interventions and in the instruments used to measure HL. The effectiveness of the interventions varied between studies, with improvements that were diminished over time or insufficient in participants with initial low literacy. CONCLUSION: The evidence to date in interventions oriented to study HL in patients with cancer is focused on other constructs, leaving HL as a phenomenon difficult to define both conceptually and clinically. Variability in designs and measurements makes comparison between interventions difficult. Defining and operationalizing HL is critical to design and measure effective interventions, which must be adapted to patients' needs.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".