MJM MedTalks (S01E01): A Conversation with Dr. Samir Shaheen-Hussain
Bibliographic record
Abstract
McGill Journal of Medicine (MJM) MedTalks is a Podcast series where members of the McGill Faculty of Medicine and Health Sciences are interviewed on topics related to career, research, advocacy and more. The aim of MedTalks is to open a space where faculty members can share information and advice for trainees in healthcare and medical sciences. In this episode, McGill medical student Susan Wang interviews Dr. Samir Shaheen-Hussain about his new book, “Fighting for a Hand to Hold* Confronting Medical Colonialism Against Indigenous Children in Canada”. The conversation is divided into three parts: 1) personal questions about Dr. Shaheen-Hussain’s work in social justice, 2) questions regarding the book, in particular on aspects of anti-indigenous systematic racism and its interplay in Canada’s current healthcare system, and 3) advocacy and burnout-related advice for medical trainees. This podcast interview is also accompanied by a book review and reflection, titled “A Medical Student’s Perspective on Fighting for a Hand to Hold”, available at mjmmed.com. The show notes include a transcript of the podcast, time stamps, and resources and references. This podcast is produced and edited by MJM’s social media team, Tom Lee, Saman Arfaie and Susan Wang and transcribed by S. Wang.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.038 | 0.006 |
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".