“Formulation wars”: a novel formulation curriculum for residents and faculty
Bibliographic record
Abstract
Biopsychosocial formulation remains an important skill for both residents and faculty. If it is not taught early and adequately, then residents fail to develop this skill. Despite a number of evidence-based teaching tools, residents continue to voice concern about when and how formulation is being taught in training programs. A survey in Canada showed that residents were dissatisfied with the current “status quo”. Structured teaching was deemed important; as was hearing supervisors formulate. Small group teaching was valued and early exposure was also considered beneficial. The purpose of our paper is to demonstrate a novel technique for teaching biopsychosocial formulation to psychiatry residents of all training levels. We detail a workshop we developed for both residents and faculty that combines faculty formulations with small and large group work. We recognize that this initial workshop was a small first step in changing the culture of formulation teaching. More studies are needed to determine exactly which teaching methods should be employed in a more robust and structured formulation curriculum.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".