Exploring How Best to Teach Trauma-Informed Care in Undergraduate Medical Education
Bibliographic record
Abstract
Trauma-informed care (TIC) is an approach that recognizes the potential for patients to have experienced trauma and requires care to be sensitive and adaptive to this; ongoing calls to action continue to highlight the need for TIC to be incorporated into the training of medical doctors. This exploratory qualitative study employed constructivist grounded theory methods to empirically investigate how leading physicians in Canada conceptualize and operationalize TIC and further examine how it could be effectively taught to medical learners during undergraduate medical education (UME). The study found that physicians view TIC as a practice philosophy, rather than a specific framework or set of skills, oriented around seven principles. Rather than viewing trauma as a biomedical or psychiatric condition, physicians saw structures and systems of oppression as mediators for – and causes of – trauma. Findings illuminate foundational knowledge and skills necessary to augment the translation of TIC in clinical practice that can be used to inform what and how medical schools teach TIC. This study identified the importance of longitudinal integration, spirality, and meaningful applications of TIC in a UME-level TIC curriculum, which ensures that all learners are introduced to the construct of TIC and are able to apply it in early clinical interactions. However, challenges such as the contradictory and powerful influence of the hidden curriculum as well as the critical need for faculty development must be addressed. Overall, this emphasizes the need for physician training to cultivate context- dependent and adaptable approaches to TIC in an effort to break the cycle of systemic violence and trauma in medicine.
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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 | 0.014 | 0.025 |
| 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 | 0.011 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".