Knowledge and training in paediatric medical traumatic stress and trauma-informed care among emergency medical professionals in low- and middle-income countries
Bibliographic record
Abstract
Background: Provision of psychosocial care, in particular trauma-informed care, in the immediate aftermath of paediatric injury is a recommended strategy to minimize the risk of paediatric medical traumatic stress.Objective: To examine the knowledge of paediatric medical traumatic stress and perspectives on providing trauma-informed care among emergency staff working in low- and middle-income countries (LMICs).Method: Training status, knowledge of paediatric medical traumatic stress, attitudes towards incorporating psychosocial care and barriers experienced were assessed using an online self-report questionnaire. Respondents included 320 emergency staff from 58 LMICs. Data analyses included descriptive statistics, t-tests and multiple regression.Results: Participating emergency staff working in LMICs had a low level of knowledge of paediatric medical traumatic stress. Ninety-one percent of respondents had not received any training or education in paediatric medical traumatic stress, or trauma-informed care for injured children, while 94% of respondents indicated they wanted training in this area.Conclusions: There appears to be a need for training and education of emergency staff in LMICs regarding paediatric medical traumatic stress and trauma-informed care, in particular among staff working in comparatively lower income countries.
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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 | 0.002 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".