A Map of the Current Cultural Climate in Medicine and Healthcare, and How We Can Change It
Bibliographic record
Abstract
In Canada, healthcare and medicine are grounded in structures of coloniality, oppression, heteropatriarchy and a variety of “-isms” (racism, sexism, ableism, classism). Consequently, it is little wonder that deep-rooted, enduring health disparities exist for many different groups across Canada. The COVID-19 pandemic has only served to exacerbate these disparities. Clearly, something needs to change in healthcare delivery and education. Comics are an ideal medium to document this moment, as well as catalyze change, for many reasons. Graphic Medicine – the creation and study of comics in healthcare contexts – can be used to explore the current discourses and cultures of healthcare and bring diverse perspectives into dialogue. Comics are also inherently disruptive. They challenge what is considered acceptable as discourse, and therefore knowledge, within medicine. They are also accessible to anyone with a writing tool, surface, and an idea to share. In this way, comics help democratize communication and give oft-ignored voices the ability to help shape medical discourse. Additionally, the diversity of forms and features used in comics creation directly relates to and enhances the diversity of voices, perspectives and lived experiences expressed in comics. Graphic Medicine can be a tool to advocate for health equity across populations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.021 | 0.043 |
| Scholarly communication | 0.027 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 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".