Universal Measures of Support Are Needed: A Cross-Sectional Study of Health Literacy in Patients with Dupuytren’s Disease
Bibliographic record
Abstract
BACKGROUND: Health literacy represents the degree to which patients can understand and act on health information. The relevance of health literacy to health care delivery, outcomes, and overall surgical care is unambiguous. This study aimed (1) to determine the prevalence of limited health literacy in patients diagnosed with Dupuytren's contracture and (2) to identify independent predictors of limited health literacy. METHODS: This cross-sectional study included patients with Dupuytren's disease and with self-reported English fluency. The Newest Vital Sign, a rapid, validated, and reliable screening tool, was selected to measure health literacy. An exploratory multivariable logistic regression model was used to identify possible predictors of limited health literacy. RESULTS: A total of 185 patients met eligibility criteria and were included. From those, 82 (44 percent) were found to have limited health literacy, defined as a score of 3 or less on the Newest Vital Sign. The domain of prose literacy was most highly scored compared to numeracy and document literacy. Lower household income was associated with a 4.7-fold increase in the odds of having limited health literacy. Being an immigrant also increased the odds of having limited health literacy by a factor of 3.6. Sensitivity analyses and subgroup analyses (based on education, maternal language, and immigration status) corroborated these independent predictor findings. CONCLUSIONS: Limited health literacy is common among patients with Dupuytren's contracture. System level changes are necessary such as the access and integration to clinical care of universal measures of support to promote productive patient-surgeon interactions.
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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".