Measuring Trauma- (and Violence-) Informed Care: A Scoping Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it