Impact of cardiovascular risk visualization on motivation to self-manage young-onset type 2 diabetes
Bibliographic record
Abstract
AIMS: To investigate the accessibility, credibility, emotional responses, and impacts of cardiovascular risk visualizations in motivating self-management of young-onset type 2 diabetes (YOD). METHODS: We conducted a mixed-methods study among adults with YOD (age at diagnosis < 40 years, disease duration < 10 years) in Ontario, Canada. We created 6 visualizations (5-year risk: linear scale, icon array, bar graph; lifetime risk: bar graph, icon array; cardiovascular age). We measured numeracy, graph literacy, accessibility, credibility, and emotional responses. We used linear regression to measure associations among these variables. We conducted semi-structured interviews to explore impacts on motivation. RESULTS: We included 31 participants (54.8% women, mean age 37.5 years). The lifetime risk bar graph and icon array received the highest accessibility (P = 0.008), similarly high credibility (P = 0.1), the highest negative emotion (P = 0.004), and similar positive emotion (P = 0.6) scores compared to the other visualizations. The lifetime risk visualizations evoked feelings of urgency or fear, which strongly enhanced motivation. Only the lifetime risk bar graph had high accessibility and credibility scores across all levels of numeracy and graph literacy. Graph literacy was negatively associated with positive emotion. CONCLUSIONS: Lifetime risk visualizations, especially bar graphs, are more accessible and emotionally evocative in potentially motivating self-management of YOD than other visualizations.
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.007 | 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".