Celebrating a Decade of the HFA Young Community: Achievements and Future Directions by the HFA Young Committee
Bibliographic record
Abstract
The Heart Failure Association (HFA) Young initiative, established in 2014, welcomes all HFA members under 40 years of age, including physicians, scientists, nurses and allied health professionals. Its primary aim is to foster the growth of the next generation of heart failure (HF) specialists through a variety of educational and career-building programmes such as the monthly HFA Cardiotalk Podcast, the quarterly HFA Journal Club and the Career Café. The members also have the possibility to participate in the construction of the annual HFA congress programme and benefit from travel grants for the HFA Congress, and networking events at scientific meetings. In 2022-2023, the HFA Young conducted a survey that garnered 305 members, giving an important snapshot of their needs, expectations and aspirations, which served as a roadmap for the priorities of the group. Finally, in order to build and maintain networks of young professionals at the national level, the HFA Young Ambassadors initiative was established, connecting the HFA Young Committee with young HF professionals in their respective countries. This initiative has proven to be crucial for building a global community of emerging HF specialists and enhancing the awareness of the HFA's activities. The article presents the evolution of HFA Young over the past 10 years, summarizes key activities and survey results and seeks to outline future development directions.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".