Trauma-informed knowledge, awareness, practice, competence and confidence of rural health staff: A descriptive study
Bibliographic record
Abstract
Background and objective: By adopting a trauma-informed approach to care at the organisational and clinical levels, health care systems and providers can enhance the quality of care that they deliver and improve health outcomes for individuals with a trauma history. This study aimed to explore the trauma-related knowledge, attitudes awareness, practice, competence and confidence of health service staff from three small rural health services in Victoria, Australia, and examine their self-reported capacity to respond to clients with a trauma history.Methods: Staff from each site were invited to complete a paper-based survey. The survey included demographic information and questions related to knowledge and understanding of trauma, experience of trauma-informed care and confidence engaging in, and perceived importance of, trauma-informed practices. Results: The respondents were predominately nurses. Results showed that 16% of respondents had undertaken training in trauma-informed care and 44% disagreed that they had an understanding of trauma-informed practices. There were high levels of agreement for statements related to knowledge and understanding of trauma and low levels of agreement with statements related to experience of trauma-informed care. More than 70% of respondents reported that they had little knowledge of the principals of trauma-informed care, and little experiencing with practicing trauma-informed care.Discussion and conclusions: Overall, the survey results showed that staff were trauma-aware, but supported the need for more education and training in trauma-informed practices and improved organisational approaches to support trauma-informed approaches. It is important for organisations to shift from being trauma aware to being trauma-informed, by building foundational awareness of these practices and reinforcement through continuing education.
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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 | 0.006 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".