Measuring Trauma- (and Violence-) Informed Care: A Scoping Review
Bibliographic record
Abstract
Trauma- (and violence-) informed care (T(V)IC) has emerged as an important practice approach across a spectrum of care settings; however how to measure its implementation and impact has not been well-examined. The purpose of this scoping review is to describe the nature and extent of available measures of T(V)IC, including the cross-cutting concepts of vicarious trauma and implicit bias. Using multiple search strategies, including searches conducted by a professional librarian from database inception to Summer 2020, 1074 articles were retrieved and independently screened for eligibility by two team members. A total of 228 were reviewed in full text, yielding 13 measures that met pre-defined inclusion criteria: 1) full-text available in English; 2) describes the initial development and validation of a measure, that 3) is intended to be used to evaluate T(V)IC. A related review of vicarious trauma measures yielded two that are predominant in this literature. Among the 13 measures identified, there was significant diversity in what aspects of T(V)IC are assessed, with a clear emphasis on "knowledge" and "safety", and less on "collaboration/choice" and "strengths-based" concepts. The items and measures are roughly split in terms of assessing individual-level knowledge, attitudes and practices, and organizational policies and protocols. Few measures examine structural factors, including racism, misogyny, poverty and other inequities, and their impact on people's lives. We conclude that existing measures do not generally cover the full potential range of the T(V)IC, and that those seeking such a measure would need to adapt and/or combine two or more existing tools.
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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 | 0.040 | 0.189 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.031 | 0.031 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".