Childhood Post-Traumatic Stress Disorder: A Self-Directed Learning Module
Bibliographic record
Abstract
Abstract Introduction Post-traumatic stress disorder (PTSD) can lead to significant morbidity, mortality, and illness burden. This resource was developed for use by medical students as a part of the Clinical Simulation Initiative (CSI) in Psychiatry. The CSI taskforce was developed in 2010 by the Association of Directors of Medical Student Education in Psychiatry to create a free national database of online psychiatry self-directed learning modules. A module on PTSD in the adult population has already been published. This resource is distinct and addresses the impact of early childhood trauma on development and presentation of PTSD in children and adolescents. Methods We used Articulate software to develop a self-directed learning module that includes video clips of an interview with a child and teen actor representing the same patient with PTSD at different ages. The module helps students learn about the developmental effects of trauma on children and adolescents and also guides students through formulating a management plan for traumatized children and youth. An outline of key aspects of the etiology, epidemiology, diagnosis, and treatment is provided, along with self-assessment quizzes related to the module's learning objectives. The module contains a brief survey at the end to obtain feedback. Results This module is currently used at our institution by all Year 3 medical students during their psychiatry clerkship. We have over 200 students per class at distributed sites throughout our province. The module allows for equitable curriculum delivery to all students. It teaches some basic components of the psychiatric assessment of a child, which is a core psychiatry and pediatrics skill, and introduces students to the important concept of developmental trauma. It was successfully launched in September 2013 and has received positive reviews from students and faculty. Discussion This module is not intended to replace exposure to real patients with PTSD, but it can be used to augment clinical exposure and help acquire basic knowledge of the illness. We plan to use student feedback and further research and clinical developments to update and improve the module on a yearly basis. Although designed for medical students, this resource could likely be used for other learners.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.011 |
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".