Bibliographic record
Abstract
) editorial with reflections on the current landscape in healthcare and what is top of mind for healthcare leaders. In this issue, we are instead starting with two welcomes and a fond farewell. Our first welcome is for Richard Lewanczuk as our new co-editor-in-chief alongside Anne Wojtak. Richard is the senior medical director of Health System Integration for Alberta Health Services and was previously the senior medical director of Primary Care for the same organization. He is also a professor emeritus in the Department of Medicine at the University of Alberta where he continues to co-chair the Social Determinants of Health Working Group. Our second welcome is for Ruby Brown who is a special guest editor for our theme on mental health (Brown and Wojtak 2024). Ruby has led health systems across several provinces and territories, with an unrelenting commitment to improve the state of mental health. She believes that the key to tackling the complexity of mental health and substance use lies in our ability to maintain consistent and steadfast cooperation across all segments of society. By sharing knowledge, we can gain deeper insights into national and global proceedings, which, in turn, enlighten and obligate us all to implement approaches essential for the greater well-being of Canadians. We are excited to have both of these wonderful leaders join our editorial team.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.099 | 0.092 |
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".