Views, self-rated competency, and perceived barriers in practicing trauma-informed care: A survey of Physician Assistants in Canada
Bibliographic record
Abstract
Psychological trauma has a widespread impact on individuals and the healthcare system, with it being estimated that over 70% of Canadians have experienced a traumatic event in their lives (1). Trauma-informed care (TIC) acknowledges the impact that trauma can have on an individual, works to understand the effects of trauma, recognizes the signs and symptoms of traumatic stress, and works to actively resist re-traumatization. The purpose of this study was to assess the opinions, self-rated competency, and perceived barriers of Canadian Physician Assistants (PAs) towards their practice of trauma-informed care. A survey study was distributed via email and various social media groups with a total of 66 respondents. The majority of participants had positive opinions towards TIC, feel somewhat confident in their practice of TIC and expressed a desire to learn more about it. Participants also acknowledged various barriers to the implementation of TIC, including a lack of training and education on the topic. In conclusion, there appears to be a knowledge gap between Canadian PAs and the practice of TIC, but the positive reception and interest towards the topic suggests this is a promising area for future growth and education for PAs in Canada.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".