A student-led educational activity on trauma informed care: Reflections and recommendations.
Bibliographic record
Abstract
OBJECTIVE: The prevalence of trauma exposure is remarkably high globally. In Canada, for example, it is estimated that over 75% of the national population have experienced a traumatic event during their lifetime. While exposure to trauma is linked with a number of poor health-related problems, the teaching of trauma informed care (TIC) principles across Canadian medical schools is uncertain, and many primary care providers (PCP) feel underprepared to have discussions regarding trauma with their patient. The purpose of this report is to promote the teaching and better knowledge of TIC among health professional learners. METHOD: A team of medical and health sciences students at a Canadian university hosted a virtual, interdisciplinary and educational Trauma Informed Care Conference (TICC) that was targeted toward health care learners using an interactive panel of interdisciplinary experts. RESULTS: A total of 107 participants attended the TICC. Select lectures were presented to improve participants' knowledge of TIC through a myriad of lenses, including historical, cultural, developmental/perinatal, intergenerational, and system-oriented sources. TIC was presented as promoting trauma awareness, trust, safety, collaboration, autonomy, and a strengths-based approach to care. CONCLUSIONS: We advocate for better awareness of TIC, the associated trauma theories, and implementation of the core values and principles of TIC in the training of health professionals. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.022 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".