Patient judgments about hypertension control: the role of patient numeracy and graph literacy
Bibliographic record
Abstract
OBJECTIVE: To assess the impact of patient health literacy, numeracy, and graph literacy on perceptions of hypertension control using different forms of data visualization. MATERIALS AND METHODS: Participants (Internet sample of 1079 patients with hypertension) reviewed 12 brief vignettes describing a fictitious patient; each vignette included a graph of the patient's blood pressure (BP) data. We examined how variations in mean systolic blood pressure, BP standard deviation, and form of visualization (eg, data table, graph with raw values or smoothed values only) affected judgments about hypertension control and need for medication change. We also measured patient's health literacy, subjective and objective numeracy, and graph literacy. RESULTS: Judgments about hypertension data presented as a smoothed graph were significantly more positive (ie, hypertension deemed to be better controlled) then judgments about the same data presented as either a data table or an unsmoothed graph. Hypertension data viewed in tabular form was perceived more positively than graphs of the raw data. Data visualization had the greatest impact on participants with high graph literacy. DISCUSSION: Data visualization can direct patients to attend to more clinically meaningful information, thereby improving their judgments of hypertension control. However, patients with lower graph literacy may still have difficulty accessing important information from data visualizations. CONCLUSION: Addressing uncertainty inherent in the variability between BP measurements is an important consideration in visualization design. Well-designed data visualization could help to alleviate clinical uncertainty, one of the key drivers of clinical inertia and uncontrolled hypertension.
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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 | 0.006 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".