Health literacy and cancer self‐management behaviors: A scoping review
Bibliographic record
Abstract
BACKGROUND: Increasing demands on health care systems require patients to take on more active roles in their health. Effective self-management has been linked to improved health outcomes, and evidence shows that effective self-management is linked to health literacy (HL). HL is an important predictor of successful self-management in other chronic diseases but has had minimal testing in cancer. METHODS: A scoping review was conducted to examine and summarize what is known about the association between HL and self-management behaviors and health service utilization in the cancer setting. The methodological framework articulated by Arksey and O'Malley was used and was further refined with the Joanna Briggs Institute methodology. Inclusion criteria included the following: peer review; publication in English; and adult patients and caregivers of all races, ethnicities, and cultural groups. Use of a validated instrument to measure HL was required. RESULTS: The search yielded 2414 articles. After the removal of duplicates and the performance of title scans and abstract reviews, the number was reduced to 44. Of the 44 full-text articles reviewed, 17 met the inclusion criteria. A number of important self-management behaviors and related outcomes were found to be associated with HL. These included the uptake of cancer screening, the receipt of prescribed chemotherapy, and a greater risk of postoperative complications. CONCLUSIONS: This literature review shows that HL is associated with important self-management behaviors in cancer. The implications of these associations for individuals with inadequate HL and for the health care system are significant. More research is needed to explore these associations.
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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 | 0.011 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.022 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".