Qualitative Assessment of a virtual cardiovascular medical education Program in Haiti: local physicians in-training experiences
Bibliographic record
Abstract
BACKGROUND: Haiti faces challenges in accessing equitable healthcare and medical education due to limited resources. Cardiovascular disease burden is high, necessitating a well-trained cardiovascular workforce. The International Cardiology Curriculum Accessible by Remote Distance Learning (ICARDs) program, launched in 2019, addresses this need by providing virtual cardiovascular education. This study aimed to explore the perceptions and experiences of internal medicine residents and physicians participating in the ICARDs program, focusing on their expectations, facilitators, barriers, and recommendations for improvement. A qualitative research approach was adopted, conducting three focus groups with participants in three different hospitals across Haiti. METHODS: The study utilized a Unified Theoretical Framework of Learning Theories as a structured framework to identify themes. A deductive content analysis was employed to identify barriers and facilitators and valuable information from participants' responses. RESULTS: Participants expressed high expectations and reported positive experiences with the ICARDs program. They acknowledged its positive impact on patient care and the development of their medical skills. However, some concerns were raised regarding course content and irregularities in the program. CONCLUSIONS: The ICARDs program fulfills participants' expectations for cardiovascular education and fosters a supportive community. To enhance its effectiveness, addressing content concerns and infrastructure limitations is essential. The study's findings provide valuable insights for program organizers to tailor the ICARDs program and better meet the participants' needs.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".