Storylines of Trauma in Health Professions Education: A Critical Metanarrative Review
Bibliographic record
Abstract
PHENOMENON: Learners in medical education are often exposed to content and situations that might be experienced as traumatic, which in turn has both professional and personal implications. The purpose of this study was to synthesize the literature on how trauma has been conceptualized and approached within medical education, and the implications thereof. APPROACH: A metanarrative approach was adopted following the RAMESES guidelines. Searches of 7 databases conducted in January 2022 with no date limitations yielded 7,280 articles, of which 50 were identified for inclusion through purposive and theoretical sampling. An additional 5 articles were added from manual searches of reference lists. Iterative readings, interpretive and reflexive analysis, and research team discussions were performed to identify and refine metanarratives. FINDINGS: Five metanarratives were identified, including the concept of trauma, the trauma event, the person with trauma, the impact of trauma, and addressing trauma, with each metanarrative encompassing multiple dimensions. A biomedical concept of trauma predominated, with lack of conceptual clarity. Theory was not integrated or developed in the majority of articles reviewed, and context was often ambiguous. Trauma was described in myriad ways among studies. Why certain events were experienced as trauma and the context in which they took place were not well characterized. The impact of trauma was largely concentrated on harmful effects, and manifestations beyond symptoms of post-traumatic stress were often not considered. Furthermore, the dominant focus was on the individual, yet often in a circumscribed way that did not seek to understand the individual experience. In addressing trauma, recommendations were often generic, and earlier research emphasized individually-focused interventions while more recent studies have considered systemic issues. INSIGHTS: Multiple dimensions of trauma have been discussed in the medical education literature and from many conceptual standpoints, with biomedical, epidemiologic, and individualized perspectives predominating. Greater precision and clarity in defining and understanding trauma is needed to advance research and theory around trauma in medical education and the associated implications for practice. Exploring trauma from intersectional and collective experiences and impacts of trauma and adapting responses to individual needs offers ways to deepen our understanding of how to better support learners impacted by trauma.
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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 | 0.088 | 0.241 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.038 | 0.017 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".