Development of the oppression-based traumatic stress inventory: a novel and intersectional approach to measuring traumatic stress
Bibliographic record
Abstract
There is a growing body of literature demonstrating that experiences of oppression (e.g., racism, sexism, heterosexism, poverty) are associated with posttraumatic stress disorder symptoms. Traditional trauma assessments do not assess experiences of oppression and it is therefore imperative to develop instruments that do. To assess oppression-based traumatic stress broadly, and in an intersectional manner, we have developed the oppression-based traumatic stress inventory (OBTSI). The OBTSI includes two parts. Part A comprises open-ended questions asking participants to describe experiences of oppression as well as a set of questions to determine whether Criterion A for PTSD is met. Part B assesses specific posttraumatic stress symptoms anchored to the previously described experiences of oppression and also asks participants to identify the various types of discrimination they have experienced (e.g., based on racial group, sex/gender, sexual orientation, etc.). Clients from a mental health clinic and an undergraduate sample responded to the OBTSI and other self-report measures of depression, anxiety, and traditional posttraumatic stress (N = 90). Preliminary analyses demonstrate strong internal consistency reliability for the overall symptom inventory (α = 0.97) as well as for the four symptom clusters of posttraumatic stress symptoms in the DSM-5 (α ranging from 0.86 to 0.94). In addition to providing descriptive information, we also assess the convergent validity between the OBTSI and measures of anxiety, depression, and traditional posttraumatic stress and examine the factor structure. This study provides preliminary evidence that the OBTSI is a reliable and valid method of assessing oppression-based traumatic stress symptoms.
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.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".