Post‐traumatic stress disorder symptoms among emergency nurses: their perspective and a ‘tailor‐made’ solution
Bibliographic record
Abstract
AIM: The goal of this study was to identify support activities for emergency room nurses who have been exposed to traumatic events, in order to prevent post-traumatic stress disorder. BACKGROUND: Emergency room nurses experience stress during traumatic events, for which they need support. It turns out that such support is insufficient, ineffective or non-existent. METHODS: This qualitative study was carried out among twelve emergency room nurses from January to May 2007. Semi-structured interviews and a focus group were conducted. Content analysis fulfilled the objectives of our research. RESULTS: The frequency of traumatic events leading to contextual exposure and exposure as a witness increases with years of experience (r=0·67 and r=0·57). The frequency of post-traumatic stress disorder symptoms decreases with age (r=-0·83). The data demonstrate the importance of having a supportive social network and being able to talk things over with colleagues. The support activities considered include all types of prevention. They consist primarily of a peer support system, psycho-education and emergency room simulations. CONCLUSION: A three-level complex of support activities represents a promising solution to prevent and treat post-traumatic stress disorder among emergency room nurses. A further study to test its effectiveness is currently underway.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".