Validation of the Sensitivity to Pain Traumatization Scale
Bibliographic record
Abstract
The present dissertation examines the psychometric properties of the Sensitivity to Pain Traumatization Scale (SPTS-12), a measure developed to assess the cognitive, emotional, behavioural, and somatic responses to pain that are similar to a traumatic stress response. The literature review provides a description of the definition, models, and burden of chronic pain and trauma, as well as a discussion of the high rates of comorbidity between chronic pain and trauma. Next, common pain-related anxiety measures are described followed by a summary of the development of the SPTS-12. Three studies are presented that examine the psychometric properties of the SPTS-12. Study 1 evaluates the factor structure, reliability, and validity of the SPTS-12 in a sample of 823 undergraduate students who were pain-free or reported experiencing ongoing pain. For both groups, the one-factor model demonstrated adequate overall fit and the SPTS-12 total score showed excellent reliability and good convergent validity with a measure of trauma symptoms, with mixed findings regarding the divergent validity of the SPTS-12 when examined against a measure of depressive symptoms. Study 2 explores the factor structure, reliability, and validity of the SPTS-12 in a clinical sample of 180 patients receiving care in an outpatient multidisciplinary service designed to help prevent the development of chronic postsurgical pain. Confirmatory factor analysis supported the one-factor model of the SPTS-12, with evidence of excellent internal consistency reliability. The SPTS-12 demonstrated good convergent validity, but divergent validity was not supported. Study 3 uses latent class mixed models to represent trajectories of SPTS-12 scores in a clinical sample of 361 patients after surgery. The optimally-fitting model consisted of five SPTS-12 trajectories, three of which were characterized by significantly decreasing scores over time. Analysis of pain-related outcomes predicted by SPTS-12 trajectories provide evidence of criterion validity of the SPTS-12. Across all three studies, the results indicate that the SPTS-12 provides a way to more directly measure traumatization that individuals may experience in response to pain, which may contribute to our understanding of why trauma and pain co-occur so frequently. Given the high incidence of pain and trauma, as well as the established efficacy of psychotherapy in treating pain after surgery, the present results suggest that tailoring treatment to better address trauma-specific symptoms may help improve pain management treatment strategies. Limitations include several large residual correlations between some items of the SPTS-12 in Study 1. Furthermore, in all three studies, the samples were highly heterogeneous and may not have identified differences among distinct subsamples. Additionally, missing data may have contributed to a systematic bias that only captures participants who provided adequate responses. Possible future directions include developing alternate wording for the item with the poorest fit on the SPTS-12, evaluating the concurrent validity of the SPTS-12 by examining its relationship with clinically relevant mental health diagnoses, and validating the SPTS-12 in different patient and community populations. In summary, the present dissertation provides evidence of strong psychometric properties of the SPTS-12 and encourages ongoing refinement and validation of the scale.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".