Realize, Recognize, Respond: The Building of Trauma-Informed Care in Medicine
Bibliographic record
Abstract
In December 2019, Western University delivered its first lecture on trauma-informed care (TIC) to its medical students. TIC is a universal treatment framework that involves realizing, recognizing, and responding to the effects of emotional trauma. The model shifts the question from “what is wrong with you?” to “what has happened to you?” This change in approach has only recently gained traction among medical professionals, perhaps because until recently, post-traumatic stress disorder (PTSD) was predominantly understood within the context of military trauma while much less was known about the pervasiveness of trauma within the civilian domain. Consequently, historians have largely focused on the medical community’s understanding of psychological trauma in the wake of the Vietnam War. Indeed, combat-related psychiatric research helped shape early ideas of trauma, but there existed concurrent driving forces that have been mostly overlooked. Knowledge of a more complete historical narrative of the civil origins of TIC may serve as a powerful tool to create genuine empathy towards patients with past trauma. Drawing on journal articles, newspaper archives, and physician questionnaires from the early 1970s to the present, this paper demonstrates the significant contributions made by the women’s movement in forming the official diagnosis of PTSD, in dismantling taboos surrounding domestic violence, and in initiating the move toward TIC. This paper concludes with a discussion of the important role that TIC training in medical school curriculums may hold, followed by an analysis of barriers to the implementation of TIC within clinical practice, and finally, suggested future directions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.078 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.021 | 0.070 |
| Scholarly communication | 0.024 | 0.031 |
| Open science | 0.005 | 0.054 |
| Research integrity | 0.012 | 0.031 |
| Insufficient payload (model declined to judge) | 0.005 | 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".