An Epidemic of Incompetence: A Critical Review of Addictions Curriculum in Canadian Residency Programs
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. In Canada and the United States, the rising number of apparent opioid-related deaths have given to the aptly-named opioid epidemic. Despite the criticism physicians have received for their role in opioid overprescribing, physicians may very well be in the position to vanquish the opioid epidemic. While the importance of the importance of Addictions training in psychiatry and other disciplines has been recognized in Canada at a national level, training resources are scarce and difficult to implement, even when delivered in online formats. Many have speculated that the delivery of high-quality Addictions training has been hampered by multiple roadblocks endemic to the Canadian medical education system, particularly stigma towards individuals with substance use disorders. In navigating the winds of change in the Competency-Based Medical Education (CBME) era, it remains unclear how Addictions will be embraced. To date, there are no defined addictions competencies in the Canadian CBME infrastructure, despite the critical findings of the Association of Faculties of Medicine report in 2017, which was generated in response to the opioid epidemic. Despite these challenges, those who struggle with addiction can lead full, happy, productive lives if they have the right resources. With time, we can only hope that the increasing visibility of addiction will translate to improved training and curricula for the next generation of physicians.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".