Advancing Integrated Care through Psychiatric Workforce Development: A Systematic Review of Educational Interventions to Train Psychiatrists in Integrated Care
Bibliographic record
Abstract
OBJECTIVE: Integrated or collaborative care is a well-evidenced and widely practiced approach to improve access to high-quality mental health care in primary care and other settings. Psychiatrists require preparation for this emerging type of practice, and such training is now mandatory for Canadian psychiatry residents. However, it is not known how best to mount such training, and in the absence of such knowledge, the quality of training across Canada has suffered. To guide integrated care education nationally, we conducted a systematic review of published and unpublished training programs. METHOD: We searched journal databases and web-based 'grey' literature and contacted all North American psychiatry residency programs known to provide integrated care training. We included educational interventions targeting practicing psychiatrists or psychiatry residents as learners. We critically appraised literature using the Medical Education Research Study Quality Instrument (MERSQI). We described the goals, content, and format of training, as well as outcomes categorized according to Kirkpatrick level of impact. RESULTS: We included 9 published and 5 unpublished educational interventions. Studies were of low to moderate quality and reflected possible publication bias toward favourable outcomes. Programs commonly involved longitudinal clinical experiences for residents, mentoring networks for practicing physicians, or brief didactic experiences and were rarely oriented toward the most empirically supported models of integrated care. Implementation challenges were widespread. CONCLUSIONS: Similar to integrated care clinical interventions, integrated care training is important yet difficult to achieve. Educational initiatives could benefit from faculty development, quality improvement to synergistically improve care and training, and stronger evaluation. Systematic review registration number: PROSPERO 2014:CRD42014010295.
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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 | 0.013 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".