Bibliographic record
Abstract
Dr. Danielle Martin is an Associate Professor at the University of Toronto and Executive Vice President and Chief Medical Executive at Women’s College Hospital in Toronto, where she is also a practicing family physician. Her career epitomizes an idea that is often discussed in medical training, but rarely manifests in practice: physicians are both advocates for individual patients and stewards of public health equity at a systems level. Dr. Martin has dedicated her career to improving and strengthening Canada’s universal health care system. She is a public leader in the ongoing debate about health care privatization and founded the organization Canadian Doctors for Medicare. Notably, Dr. Martin spoke about Canadian health care and advocated for single-payer health care in a widely publicized US Senate hearing led by Senator Bernie Sanders. She has also published a book titled, Better Now: Six Big Ideas to Improve Health Care for All Canadians, which breaks down complex health policy into six actionable steps in order to improve the health care system for all Canadians. She continuously advocates for public involvement in health policy through research and public outreach and is a role model for young physicians aspiring to leadership roles in health and health care policy, while simultaneously pursuing a career in clinical practice. In fact, doing both provides a unique framework for improving the individual wellbeing and health of Canadians. Dr. Martin believes that advocacy is a skill that requires practice and training; she advises young trainees that the time to start is now.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.032 | 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".