Mobile-healthcare application on the Cardiovascular Health Awareness Program
Bibliographic record
Abstract
Mobile-healthcare Application on the Cardiovascular Health Awareness Programby Wafa Talal Bahha Cardiovascular diseases represent the leading cause of stays in long-term services (such as hospital), it is the leading cause of death in Canada, and is associated with a number of other diseases.The Cardiovascular Health Awareness Program (CHAP) has been shown to be an effective intervention in terms of primary care services, reducing emergency room (ER) visits due to cardiovascular diseases by 9%.The purpose of this study is to harness the potential of assistive technology to promote active and healthy adults with chronic conditions in specific areas defined by CHAP.Specifically, this project aims to design and develop a system to manage cardiovascular diseases using protocols defined by CHAP.We created a C-CHAMP IPhone/IPad application for CHAP using MYSQL servers.We also used XCode6 to unify interface design, coding, testing, and debugging into a single workflow.The application was designed to serve a number of functions related to CHAP protocols, including saving each new blood pressure reading for participants in local database, collecting information about the Risk Factors, and measuring body mass index.The system was first tested in the university's lab, and then we made a survey for users, health professionals, and CHAP working groups in order to evaluate the usability, reliability, and functionality of the app.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.008 |
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".