International Congress on Academic Medicine: 2025 medical education abstracts
Bibliographic record
Abstract
The International Congress on Academic Medicine (ICAM) 2025, held from April 3-6 in Halifax, Nova Scotia, united a diverse global community engaged in academic medicine.Organized by the Association of Faculties of Medicine of Canada (AFMC), the congress aimed to foster collaboration, innovation, and progress in medical education, health research and social accountability.It provided a platform for over 1,500 delegates, including medical students, residents, graduate students, faculty, researchers, and patient partners-to meet, network, and develop new relationships and collaborations with colleagues globally.ICAM featured innovative keynotes and accredited sessions tackling global themes in academic medicine, including advocacy, artificial intelligence, social entrepreneurship, and more.Additionally, ICAM hosted international presentations and symposiums, such as the Gairdner Symposium, workshops, and oral and poster presentations.ICAM 2025 was recognized for its commitment to inclusivity, achieving "Patient Included Status," ensuring that the experiences of patients as experts in living with their conditions were incorporated into all aspects of the congress.This year, ICAM also achieved gold status as a certified sustainable event through Dalhousie University's Sustainable Events program. Congress objectives• Recognize the importance of strong connections between research, education, clinical care, and patient experiences on outcomes in medicine.• Actively integrate innovations and best practices in all facets of medical education.• Include the patient voice in all aspects of academic medicine to better meet community needs.• Engage in dialogue to promote collaboration within the international academic medicine community.Keynote: Gairdner Award Lecture: maternal and child health in the global contextThe world has made significant progress improving maternal and child health and wellbeing over the last thirty years.This year's Gairdner lecture featured recipients Zulfiqar Bhutta and José Belizan.Despite notable improvements, key challenges remain, with global progress in reducing the deaths of pregnant women, mothers, and babies having flatlined since 2015 due to decreasing investments in maternal and newborn health.With the continued need to decrease health inequities, this panel discussed current perspectives on maternal and child health in the global context, while reflecting on the pathway forward.At the end of the session, participants were able to:• Summarize key advances within the maternal and child health global health space.• Outline current challenges in maternal and child health that prevent health equity from being achieved.• Describe two health policy avenues for advancing maternal and child health.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.361 | 0.307 |
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".