A prospective assessment of PTSD symptoms using analogue trauma training with nursing students.
Bibliographic record
Abstract
© 2019 Canadian Psychological Association. Exposure to potentially traumatic workplace events is a routine component of the nursing profession, resulting in high rates of posttraumatic stress disorder (PTSD) relative to the general population. Difficulties in implementing prospective empirical research have limited the understanding of nurses' risk and resilience to PTSD; however, novel research with analogue stressors has generated useful methodology for studying risk and resilience variables associated with PTSD. The present study was designed to assess risk and resilience to PTSD in nurses using a high-fidelity trauma analogue simulation. Undergraduate nursing students (n=13) from the University of Regina participated in an immersive trauma triage training simulation. Self-report measures of risk, resilience, and trauma symptoms were completed prior to participation and 1 and 5 weeks postsimulation. Participants also reported on their subjective experiences with the analogue trauma immediately following participation. Participants described the trauma analogue as anxiety- or fear-provoking, supporting the ecological validity of trauma simulation effectiveness. Statistical analyses were limited due to low sample size, although risk variables showed theoretically valid relationships with pretrauma variables of interest. Trauma analogue simulations appear to provide an effective model for generating and understanding subclinical responses to trauma. Further research is necessary to implement such methods using sufficient samples for statistical analysis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".