Translating and piloting a cardiovascular risk assessment and management online tool using mobile technology
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.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".