Issues and challenges in the assessment, diagnosis and treatment of cardiovascular risk factors: Assessing the needs of cardiologists
Bibliographic record
Abstract
BACKGROUND: This needs assessment, initiated by the Academy for Healthcare Education Inc. in cooperation with AXDEV Group Inc., explored the knowledge, attitudes, behavior, and skills of community-based and academic-affiliated U.S. cardiologists in the area of CV risk assessment, treatment, and management from July 2006 to December 2006. METHODS: The needs assessment used a multistage, mixed-method approach to collect, analyze, and verify data from two independent sources. The exploratory phase collected data from a representative sampling of U.S. cardiologists by means of qualitative panel meetings, one-on-one interviews, and quantitative questionnaires. In the validation phase, 150 cardiologists from across the United States completed a quantitative online questionnaire. Data were analyzed with standardized statistical methods. RESULTS: The needs assessment found that cardiologists have areas of weakness pertaining to their interpersonal skills, which may influence patient-physician communication and patient adherence. Cardiologists appeared to have little familiarity with or lend little credence to the concept of relative CV risk. In daily clinical practice, they faced challenges with regard to optimal patient outcome in areas of patient referral from primary-care providers, CV risk assessment and treatment, and patient monitoring. Community-based and academic-affiliated cardiologists appeared to be only moderately interested in educational interventions that pertain to CV risk-reduction strategies. CONCLUSION: Educational interventions that target cardiologists' interpersonal skills to enhance their efficacy may benefit community-based and academic-affiliated specialists. Other desirable educational initiatives should address gaps in the patient referral process, improve patient knowledge and understanding of their disease, and provide supportive educational tools and materials to enhance patient-physician communication.
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".