The significant others’ responses to trauma scale (SORTS): applying factor analysis and item response theory to a measure of PTSD symptom accommodation
Bibliographic record
Abstract
Background: Symptom accommodation by family members (FMs) of individuals with posttraumatic stress disorder (PTSD) includes FMs’ participation in patients’ avoidance/safety behaviours and constraining self-expression to minimise conflict, potentially maintaining patients’ symptoms. The Significant Others’ Responses to Trauma Scale (SORTS) is the only existing measure of accommodation in PTSD but has not been rigorously psychometrically tested.Objective: We aimed to conduct further psychometric analyses to determine the factor structure and overall performance of the SORTS. Method: We conducted exploratory and confirmatory factor analyses using a sample of N = 715 FMs (85.7% female, 62.1% White, 86.7% romantic partners of individuals with elevated PTSD symptoms).Results: After dropping cross-loading items, results indicated good fit for a higher-order model of accommodation with two factors: an anger-related accommodation factor encompassed items related largely to minimising conflict, and an anxiety-related accommodation factor encompassed items related primarily to changes to the FM’s activities. Accommodation was positively related to PTSD severity and negatively related to relationship satisfaction, although the factors showed somewhat distinct associations. Item Response Theory analyses indicated that the scale provided good information and robust coverage of different accommodation levels.Conclusions: SORTS data should be analysed as both a single score as well as two factors to explore the factors’ potential differential performance across treatment and relationship outcomes.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".