Primary healthcare practitioners’ perspectives on trauma-informed primary care: a systematic review
Bibliographic record
Abstract
BACKGROUND: Exposure to domestic and family violence is a pervasive form of complex trauma and a major global public health problem. At the frontline of the health system, primary healthcare practitioners are uniquely placed to support individuals with experiences of trauma, yet their views on trauma-informed primary care are not well understood. This systematic review of qualitative literature sought to explore primary healthcare practitioners' perspectives on trauma-informed primary care. METHODS: Eight databases were searched up to July 2023. Studies were included if they consisted of empirical qualitative data, were conducted in general practice or equivalent generalist primary healthcare settings, and included the perspectives of primary healthcare practitioners where they could be distinguished from other participants in the analysis. Thematic synthesis was used for analysis. RESULTS: 13 papers met inclusion criteria, representing primary care settings from the United States, Canada, Australia, and Norway. Three key themes were developed: Changing the paradigm, Building trust, and Navigating the emotional load. Findings shed light on how primary healthcare practitioners perceive and strive to practise trauma-informed primary healthcare and the challenges of navigating complex, trauma-related work in the primary care environment. CONCLUSIONS: This review supports the need for recognition of the value of primary care in supporting patients with histories of trauma and violence, the development of interventions to mitigate the emotional load worn by primary healthcare practitioners, and further work to develop a deep and consistent understanding of what trauma-informed primary care encompasses.
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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 | 0.021 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".