Bedside and Community: 50 Years of Contributions to the Health of Albertans by the University of Calgary
Bibliographic record
Abstract
The University of Calgary’s innovative and collaborative approach to health care education is celebrated in an interdisciplinary collection.Bedside and Community is the inside story of fifty years of health care and health research at the University of Calgary. Drawing on the first-person accounts of researchers, administrators, faculty, and students along with archival research, and faculty histories, this collection celebrates the many significant contributions the University of Calgary has made to the health of Albertans.With contributions from the Cumming School of Medicine, the Faculty of Nursing, Faculty of Kinesiology, Faculty of Veterinary Medicine, Faculty of Environmental Design, Department of Psychology, and Indigenous Health Initiatives Bedside and Community is a truly collaborative history. Addressing the links between departments, the relationship between the university and the community, and evolving research and teaching methods, this book places the University of Calgary within a wider national context and shows how it has addressed the unique health needs of Southern Alberta.With a pioneering focus on primary care and commitment to interdisciplinary connections, the University of Calgary has made strides in health research, health education, and community outreach. Bedside and Community tells the story of a tradition of excellence that will light the way to future outreach and discovery.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".