Clinical Practices and Attitudes Regarding the Diagnosis and Management of Heart Failure: Findings from the CORE Needs Assessment Survey
Bibliographic record
Abstract
AIMS: CORE is a continuing medical education initiative designed to support the evidence-based management of heart failure (HF) in the primary and secondary care settings. The goal of the CORE Needs Assessment Survey is to describe current clinical practice patterns and attitudes among global stakeholders in HF care. METHODS AND RESULTS: The CORE Steering Committee guided the development of survey questions to assess clinical practice, confidence, and attitudes/perceptions among cardiologists, primary care physicians, and nurses involved in HF management. In total, 346 healthcare professionals from Australia (n = 59), Austria (n = 59), Canada (n = 60), Spain (n = 58), Sweden (n = 52), and the UK (n = 58) contributed survey data. Results revealed multiple gaps over the spectrum of HF care, including diagnosis (low recognition of the signs and symptoms of HF and limited use of diagnostic tests), treatment planning (underuse of recommended agents and subtherapeutic dosing), treatment monitoring and adjustment (lack of adherence to recommendations), and long-term management (low confidence in providing patient education). Although primary care and specialist physicians and nurses shared common unmet needs, healthcare professional-specific clinical gaps were also identified. CONCLUSIONS: The CORE Needs Assessment Survey provides timely data describing current clinical practices and attitudes among physicians and nurses regarding key aspects of HF care. These findings will be useful for guiding the development of interventions tailored to the specific educational needs of different provider types and designed to support the evidence-based care of patients with HF.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".