MJM MedTalks (S02E04+05): Building Healthcare: How Architecture Influences Medicine
Bibliographic record
Abstract
The McGill Journal of Medicine (MJM) Podcast Series, MJM MedTalks, interviews members of the medical and health sciences community from McGill, and beyond to gain insights into their careers, research, advocacy, and more. This series aims to enhance knowledge sharing between experts and trainees in the medical field. In this episode, Renée-Claude Bider, a Master’s student in medical physics and Podcast Associate at the McGill Journal of Medicine, interviews Prof. Annmarie Adams, who is jointly appointed in McGill University's School of Architecture and the Department of Social Studies of Medicine. Dr. Annmarie Adams trained as an architect and architectural historian at UC Berkeley. Her research focuses on how medicine, gender, and architecture intersect. In the first part of their conversation, Bider and Prof. Adams discuss the history of hospital architecture, starting in the late 1800s and focusing on Montreal and Canadian institutions, including the Royal Victoria Hospital, Montreal Neurological Institute, Montreal General Hospital, The McGill University Health Centre (Montreal, Canada), Sick Kids (Toronto, Canada) and McMaster Children’s Hospital (Hamilton, Canada). In the second part of their conversation, Bider and Prof. Adams discuss the architecture of specialized healthcare spaces, such as long-term care homes, birthing suites, palliative care, and cancer centers. They end their conversation by discussing Prof. Adams' ongoing research into the life of influential physician Maude Abbott and advice for trainees in the medical field. A glossary of terms, a content overview, a list of relevant links and research articles, supplementary images from Prof. Adams’ collection, and a transcript of the interview are included in the show notes for this episode.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.119 | 0.011 |
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".