“We need all hands on deck”: characterizing addiction medicine training in Canada—a mixed methods study of fellowship program directors
Bibliographic record
Abstract
BACKGROUND: Addiction Medicine training in Canada has evolved substantially in the last few years with the establishment of accreditation standards and several new fellowship programs. The novelty of these formal training programs, created in response to complex and ever-expanding clinical needs in Addiction Medicine, creates unique educational circumstances that must be understood to support future growth. This study characterizes the current state of these postgraduate training programs in Canada through the perspectives of Program Directors (PDs). METHODS: This study is a mixed methods study of 12 PDs. In Phase 1, participants completed a quantitative survey analyzed through descriptive statistics. In Phase 2, participants underwent a qualitative semi-structured interview that was coded with a thematic analysis approach. Mixing occurred both during the interim analysis between phases and during the interpretation stage. RESULTS: 28 trainees enrolled in a fellowship program in 2021-22 across 10 programs, and 27 trainees enrolled in 2022-23 across 11 programs. In each year, there were significantly fewer available spots than applications (31% and 29%, respectively). PDs identified a funding "bottleneck" as the most difficult and important challenge facing programs, with trainees supported by diverse and unstable funding sources. Qualitative analysis highlighted the need for sustainable funding models, flexibility toward alternative training pathways (shorter durations of training and re-entry from practice), and establishment of a national community of practice to support the co-creation of a robust addictions medical education infrastructure. CONCLUSION: For Addiction Medicine training to meet workforce demands, PDs stressed that funding was the challenge of prime importance. Future studies should examine the perspectives of Addiction Medicine fellows, the clinical and research impacts of fellowship graduates, and the cost-effectiveness of fellowship funding models.
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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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".