Interest in Rural Training Experiences in a Canadian Psychiatry Residency Program
Bibliographic record
Abstract
BACKGROUND: With the large majority of mental health professionals concentrated in urban settings, people living in rural and remote areas face significant barriers to accessing mental health care. Recognizing that early exposure is associated with future practice in rural and remote locations, we sought to obtain baseline data regarding interest in expanded rural residency training opportunities and academic teaching. METHODS: In March 2021, all psychiatry residents at the University of Alberta (UofA) were invited to complete a 19-question survey that included both closed-ended (age, gender, year of study, rural experience, interest in rural training, etc.) and open-ended questions (challenges, barriers, academic training, and other comments). A reflexive thematic analysis using an inductive and semantic approach was completed on the comments. RESULTS: 36 residents completed the survey (response rate, 75%). Significant associations were identified between previous rural training experience and interest in rural psychiatry training and practice. Female residents and junior residents were significantly more interested in rural training experiences than their counterparts. Thematic analysis noted concerns with the financial costs of accommodation and transportation, high service burden, continuity of care and isolation from their cohort. Many were interested in academic sessions on the realities of rural practice; approaches to collaborative care; and strategies on culturally relevant care; specifically Indigenous health. CONCLUSIONS: The University of Alberta has highlighted a focus on improving equity and accountability; and with a large rural catchment region; the residency program is well positioned to make training adjustments to diversify training. Based on our findings we have incorporated rural rotations for incoming residents and have developed further rural academic content to support our responsiveness and accountability to the rural and northern communities we are committed to serving. Future research should review the impact of rural training exposure in medical specialties on recruitment and retention as well as on healthcare outcomes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".