An exploration of trauma-informed care curricula in chiropractic programs: A scoping document analysis protocol
Bibliographic record
Abstract
BACKGROUND: Trauma is a significant public health issue that affects both mental and physical health. Healthcare delivery based on trauma-informed care (TIC) principles is designed to mitigate the risk of re-traumatization in healthcare settings to improve patient outcomes. Chronic pain is a common comorbidity of trauma and a common reason that people seek healthcare, including chiropractic care. The extent to which TIC training is integrated into chiropractic education and Doctor of Chiropractic Programs (DCPs) remains unclear. OBJECTIVE: This study aims to evaluate the presence of TIC principles in educational curricula documents from accredited DCPs across the United States and Canada to identify potential gaps in trauma-sensitive education within chiropractic training. METHODS: A scoping document analysis will be conducted using educational curricula documents (program handbooks, course catalogs, and course syllabi) from DCPs accredited by the Council on Chiropractic Education (CCE-USA). Documents will be evaluated for TIC-related search terms based on established frameworks from the Substance Abuse and Mental Health Services Administration and the Harvard Medical School TIC Core Competencies. The analysis will assess the presence of TIC principles such as safety, trust, empowerment, and cultural sensitivity. A phased approach will be used for data extraction, ensuring a comprehensive review of TIC integration. RESULTS: The study will quantify the inclusion of TIC principles in chiropractic education in the United States and Canada and identify trends or gaps related to TIC education. CONCLUSION: Our findings can inform future curriculum review and development, ensuring DCPs integrate TIC effectively to enhance care for trauma-exposed patients.
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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 | 0.121 | 0.130 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.032 | 0.026 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".