Bibliographic record
Abstract
To our readers, We are immensely proud to present this special issue of the McGill Journal of Medicine (MJM) focused on primary care in Quebec. In recent years the healthcare system in Quebec has gone through massive shifts, many of which have been focused on the role of family physicians and the organization of primary care within the province. These changes reflect a new understanding of the key importance of the previously ignored entry point into our advanced and increasingly complicated healthcare system.In this issue we have sought to bring together diverse perspectives in the ongoing conversation regarding the future of primary care in Quebec. We are proud to present reviews, editorials, original research, artwork and reflections from authors including Dr. Howard Bergman, the Chair of the Department of Family Medicine at McGill, as well as medical and nursing students. A special thank you to Dr. Gillian Bartlett-Esquilant, the Research and Graduate Program Director for the Department of Family Medicine, and Dr. Charo Rodriguez, Director of the McGill Family Medicine Educational Research Group, for contributing an editorial highlighting the importance of primary care research. The above editorials are only a subset of the many other fascinating pieces we are proud to publish in this Issue.This special issue would not have been possible without the incredible effort of the MJM 2016-2017 editorial team. Our editors, section editors, and web developers have worked incredibly hard to bring this project to fruition. As the MJM begins a third year after relaunching in 2015, we hope this Issue stands as a testament to its bright future. We hope you enjoy reading this issue, we have certainly enjoyed putting it together.Best, Lee H. Sterling, Editor-in-Chief, 2017-2018 Rachel La Selva and Shawn Zhuo, Editors-in-Chief, 2016-2017
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 | 0.004 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.018 | 0.022 |
| Insufficient payload (model declined to judge) | 0.036 | 0.033 |
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".