A cross-sectional needs assessment for a trauma-informed care curriculum for multidisciplinary healthcare providers
Bibliographic record
Abstract
Abstract Background Trauma-informed care (TIC) is a framework that recognizes the pervasive impact of trauma, aiming to enhance both patient outcomes and provider well-being. Given the high prevalence of trauma among individuals seeking healthcare, it is essential for healthcare providers (HCPs) to be trauma informed. However, standardized TIC curricula for training healthcare staff are lacking. This study assessed perceptions towards TIC among multidisciplinary HCPs, patients, and leadership staff at two urban hospitals in Canada. Methods This mixed-methods prospective cross-sectional study employed Kern’s six-step approach for curriculum development. A needs assessment was conducted via an online questionnaire for HCPs and semi-structed interviews with individuals from the three participant groups: HCPs, patients, and leadership staff. The questionnaire assessed knowledge, skills, and attitudes regarding TIC. Semi-structured interviews explored perspectives on TIC, including curriculum priorities and potential implementation barriers. Findings informed the development of a virtual TIC curriculum, with iterative feedback collected to refine and assess its acceptability. Results Among 106 HCP questionnaire respondents including Medical Doctors, Social Workers and Registered Nurses, 96 (90.6%) identified as women, and 97 (91.5%) as providers of direct patient care. Despite 93 (87.7%) having prior TIC education, 77 (72.6%) reported low confidence in applying TIC knowledge in clinical practice. Key perceived challenges to TIC training implementation included time constraints and lack of standardization across disciplines. A multimedia, self-paced course was the preferred solution. Thematic analysis of interviews with 28 participants (10 HCPs, 10 patients, 8 leadership staff) revealed six major themes: healthcare interactions, TIC implementation, training needs, system level barriers, curriculum preferences, and systems level improvements. Participants underscored the risk of re-traumatization to patients in healthcare settings without TIC and emphasized the need for universal TIC training for all staff. Conclusion This study revealed a strong interest in a TIC course for multidisciplinary HCPs, supports the translation of knowledge into practice and incorporates a focus on cultural humility. Integrating insights from key stakeholders in this needs assessment phase resulted in the development of a TIC curriculum inclusive of diverse voices and viewpoints and strengthened the understanding of contextual factors that will support effective TIC implementation.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".