Increasing Addiction Medicine Capacity in Canada: The Case for Collaboration in Education and Research
Bibliographic record
Abstract
Collaboration in addiction medicine education and research is important in Canada. The large unmet need for addiction services will only be met by the relatively small number of addiction clinicians, researchers, educators and policy makers in Canada if they collaborate and grow in a coordinated fashion toward common goals. Leadership from national organizations will facilitate growth in a coordinated fashion, which will allow us to reach the goal of providing adequate treatment for those with addiction and related illnesses as quickly as possible. Recent developments give significant reason to be optimistic that there will be more rapid progress in the coming years. Important initiatives currently underway include: 1) the expansion of Addiction Medicine training programs across Canada, 2) a new federal governmentsponsored initiative to build collaborations in Addictions research, and 3) an application for the first Canadian certification in Addiction Medicine based on a standardized competency based curriculum.
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 | 0.074 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.034 | 0.031 |
| Scholarly communication | 0.021 | 0.013 |
| Open science | 0.008 | 0.031 |
| Research integrity | 0.014 | 0.023 |
| Insufficient payload (model declined to judge) | 0.012 | 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".