Barriers and facilitators to implementing addiction medicine fellowships: a qualitative study with fellows, medical students, residents and preceptors
Bibliographic record
Abstract
BACKGROUND: Although progress in science has driven advances in addiction medicine, this subject has not been adequately taught to medical trainees and physicians. As a result, there has been poor integration of evidence-based practices in addiction medicine into physician training which has impeded addiction treatment and care. Recently, a number of training initiatives have emerged internationally, including the addiction medicine fellowships in Vancouver, Canada. This study was undertaken to examine barriers and facilitators of implementing addiction medicine fellowships. METHODS: We interviewed trainees and faculty from clinical and research training programmes in addiction medicine at St Paul's Hospital in Vancouver, Canada (N = 26) about barriers and facilitators to implementation of physician training in addiction medicine. We included medical students, residents, fellows and supervising physicians from a variety of specialities. We analysed interview transcripts thematically by using NVivo software. RESULTS: We identified six domains relating to training implementation: (1) organisational, (2) structural, (3) teacher, (4) learner, (5) patient and (6) community related variables either hindered or fostered addiction medicine education, depending on context. Human resources, variety of rotations, peer support and mentoring fostered implementation of addiction training. Money, time and space limitations hindered implementation. Participant accounts underscored how faculty and staff facilitated the implementation of both the clinical and the research training. CONCLUSIONS: Implementation of addiction medicine fellowships appears feasible, although a number of barriers exist. Research into factors within the local/practice environment that shape delivery of education to ensure consistent and quality education scale-up is a priority.
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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 | 0.025 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".