“Not doing it justice”: Perspectives of Recent Family Medicine Graduates on Mental Health and Addictions Training in Residency
Bibliographic record
Abstract
OBJECTIVES: Family physicians report feeling inadequately prepared to meet the evolving mental health care needs of the population. Little scholarship exists evaluating the effectiveness of curricula designed to teach mental health and addiction (MH&A) care to family medicine (FM) residents. As such, the purpose of this study was to explore the experiences of recent FM residency graduates in providing mental health care, and their perceptions of mental health training gaps during their residencies. METHODS: A qualitative descriptive study design was conducted by 8 recent graduates of the University of Toronto's FM residency program, who participated in semi-structured video interviews. A thematic analysis approach was used to collect and analyze the data. RESULTS: Through thematic analysis, 3 overarching themes were developed: (1) barriers in providing mental health and addiction care, (2) curriculum renewal, and (3) the role of FPs and professional identity. Consistent with the literature, the majority of recent FM graduates expressed discomfort when managing patients with mental health and addiction concerns. Additionally, participants perceived residency program time constraints, rotational site differences, and limited exposure to marginalized populations all impacted learning and mastery of skills. CONCLUSION: The findings of this study underscore current gaps within the FM residency curriculum and highlight the need to address current curricular deficits.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| 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".