Psychiatrists’ Engagement in Research as a Pathway towards the Expansion of Distributed Medical Education (DME): A Regional Analysis across Two Provinces in Atlantic Canada
Bibliographic record
Abstract
In the context of Canadian medical education, Distributed Medical Education (DME) plays a crucial role in addressing healthcare disparities, particularly in rural areas. This study focuses on the Department of Psychiatry at Dalhousie University, analyzing psychiatrists’ engagement and willingness to participate in research at DME sites in Nova Scotia (NS) and New Brunswick (NB). The cross-sectional study, encompassing data from an environmental scan, surveyed 60 psychiatrists involved in medical education across seven health zones. Results revealed significant associations between gender, type of graduates, and specialist training. A majority of psychiatrists (68.3%) do not currently engage in mental health or translational research, citing barriers such as a lack of protected time and financial incentives. Notably, participants expressed interest in future research areas, including health services/quality improvement and addiction research. Geriatric psychiatry, predominantly female-dominated, lacked current research activities. The study emphasizes the need to address barriers and promote motivators, both intrinsic and extrinsic, to enhance psychiatrists’ research engagement. This strategic approach is essential for fostering active participation in research, thereby contributing to the expansion of DME sites in Atlantic Canada and beyond.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".