Translating and piloting a cardiovascular risk assessment and management online tool using mobile technology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Cardiovascular disease (CVD) risk assessment and management (RAM) services face many challenges and barriers in the community. Mobile technology offers the opportunity to empower patients and improve access to health prevention strategies to overcome these barriers. However, there is limited information on the availability and use of CVDRAM-related mobile technology in the Arabic language. Objectives: To pilot test an Arabic version of a CVDRAM application among potential end-users accessing community pharmacy services in Qatar. Methodology: ·RxISK™) into the Arabic language was conducted. The English/Arabic version of the calculator was tested by potential end-users, consisting of a sample of community pharmacists (CRxs) and members of the public (MOP) accessing community pharmacy services. Semi-structured interviews were conducted based on the quality attributes of the Mobile Application Rating Scale (MARS). Data were analyzed using deductive content analysis. Results: ·RxISK™ calculator: Engagement, Functionality, Attractiveness, Education, and Responsiveness. For the most part, positive subthemes were associated with each of these themes. The functionality and educational themes had some negative subthemes. Conclusion: ·RxISK™ calculator had mostly positive descriptors that were aligned with all five quality attributes of the web and mobile applications.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it