Association of Health Literacy With Adherence to Screening Mammography Guidelines
Bibliographic record
Abstract
OBJECTIVE: To investigate the relationship of health literacy and screening mammography. METHODS: All patients seen at a breast clinic underwent prospective assessment of health literacy from January 2010 to April 2013. All women at least 40 years of age were included. Men and women diagnosed with breast cancer before age 40 years were excluded. Routine health literacy assessment was performed using the Newest Vital Sign. Demographic data were also collected. Medical records were reviewed to determine if patients had undergone screening mammography: women aged 40-49 years were considered to have undergone screening if they had another mammogram within 2 years. Women 50 years or older were considered to have undergone screening mammography if they had another mammogram within 1 year. RESULTS: A total of 1,664 consecutive patients aged 40 years or older were seen. No patient declined the health literacy assessment. Only 516 (31%) patients had undergone screening mammography. Logistic regression analysis that included ethnicity, language, education, smoking status, insurance status, employment, income, and family history found that only three factors were associated with not obtaining a mammogram: low health literacy (odds ratio [OR] 0.27, 95% confidence interval [CI] 0.19-0.37; P<.001), smoking (OR 0.64, 95% CI 0.47-0.85; P=.002), and being uninsured (OR 0.66, 95% CI 0.51-0.85; P=.001). CONCLUSION: Of all the sociodemographic variables examined, health literacy had the strongest relationship with use of screening mammography.
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".