Health numeracy: Perspectives about using numbers in health management from <scp>A</scp>frican <scp>A</scp>merican patients receiving dialysis
Bibliographic record
Abstract
Health numeracy is linked to important clinical outcomes. Kidney disease management relies heavily on patient numeracy skills across the continuum of kidney disease care. Little data are available eliciting stakeholder perspectives from patients receiving dialysis about the construct of health numeracy. Using focus groups, we asked patients receiving hemodialysis open-ended questions to identify facilitators and barriers to their understanding, interpretation, and application of numeric information in kidney care. Transcripts were analyzed using content analysis. Twelve patients participated with a mean (standard deviation) age of 56 (12) years. All were African American, 50% were women, and 83% had an annual income <$20,000/year. Although patients felt numbers were critical to every aspect in life, they noted several barriers to understanding, interpreting and applying quantitative information specifically to manage their health. Low patient self-efficacy related to health numeracy and limited patient-provider communication about quantitatively based feedback, were emphasized as key barriers. Through focus groups of key patient stakeholders we identified important modifiable barriers to effective kidney care. Additional research is needed to develop tools that support numeracy-sensitive education and communication interventions in dialysis.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".