Examining the effects of alexithymia on the relation between posttraumatic stress disorder and over-reporting.
Bibliographic record
Abstract
OBJECTIVE: Empirical examinations of the relation between posttraumatic stress disorder (PTSD) and objective measures of symptom over-reporting may be useful for identification of mechanisms of this previously observed relation. The present study examined the moderating effect of alexithymia, defined as a deficit in the ability to identify and describe emotions, on the relation between PTSD and over-reporting. METHOD: Seventy-five veterans diagnosed with PTSD were recruited from an outpatient Veterans Affairs facility and the community. Participants were administered the Miller Forensic Assessment of Symptoms Test, along with the Toronto Alexithymia Scale and PTSD Checklist within a larger study of behavioral and physiological correlates of PTSD. RESULTS: Hierarchical linear regression analyses showed a significant moderating effect of alexithymia, such that the relation between PTSD symptom severity and over-reporting was only significant in the presence of elevated alexithymia. Evaluation of the subscales of the Toronto Alexithymia Scale showed that the effect was greatest for the Difficulty Describing Emotions subscale. CONCLUSIONS: Alexithymia should be considered as a potential mechanism contributing to the over-reporting phenomena observed in the assessment and treatment of PTSD, and warrants further study. Brief interventions to improve understanding and description of emotional experiences may help to improve accuracy of symptom report. (PsycINFO Database Record
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".