Circulating Health Information toward Health Action
Bibliographic record
Abstract
Cardiovascular disease (CVD) is the most prevalent chronic disease in Canada, with 4 in 5 Canadians having at least one CV risk factor. The Canadian Cardiovascular Harmonized National Guideline Endeavour (C-CHANGE) guidelines harmonize recommendations from clinical practice guidelines to improve the prevention and treatment of CVD (Tobe et al., 2011). C-CHANGE clinicians engaged a team of healthcare design students to formulate design proposals for knowledge translation of lifestyle recommendations from the guidelines. The research question explored was how might we help people learn to self-manage their CV risk? \n \nInterviews were conducted with patients and clinicians in primary care and specialist clinics to generate insights. Clinicians participated in an initial participatory design workshop that aimed to map and understand complex health information journeys. A second co-design workshop developed proposals for intervention in the system, aided by the use of personas and storyboards. \n \nReading the health action map \nResearch findings were summarized in a system map. The circular service blueprint proposes brief bursts of healthy lifestyle information created by multidisciplinary teams and targeted to the public at multiple touchpoints. A public-facing website is proposed as an interactive and supportive online repository of evidence-based health information. The next steps would include service design and evaluation of population health impacts.
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 | 0.029 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.030 | 0.018 |
| Open science | 0.004 | 0.027 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.112 | 0.040 |
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".