Alexithymia, External Gain Expectations, and Overreporting on Symptom Validity Tests in Hospital Outpatients: Further Evidence for the Relevance of Alexithymia
Bibliographic record
Abstract
Objective: Symptom overreporting is often considered to be moderated by external incentives, such as financial or legal advantages, although other factors may also play a role. Preliminary studies have suggested a connection between symptom overreporting and alexithymia, i.e., trait-like difficulties in recognizing and describing internal sensations. This study aimed to further clarify the relationships among external gain expectations, alexithymia, and symptom overreporting. Specifically, we examined whether alexithymia is related to overreporting in patients without self-reported external gain expectations. Method: Using a cross-sectional design, patients referred for psychological assessments in a hospital setting completed a questionnaire about external gain expectations (e.g., regarding work, housing, legal issues). We differentiated between those with self-reports of external gain expectations (n = 73) and those without (n= 84). Both subsamples were administered the Toronto Alexithymia Scale-20 (TAS-20), the Structured Inventory of Malingered Symptomatology (SIMS), and the Minnesota Multiphasic Personality Inventory-2 Restructured Form (MMPI-2-RF). Results: Across the full sample, alexithymia showed a positive and statistically significant association with symptom overreporting on the SIMS and the Infrequent somatic responses scale (Fs) of the MMPI-2-RF: r = .44 and r = .31, respectively. These positive associations were also evident in the subgroup without self-reported external gain expectations (i.e., r = .35, 95% CI [.14, .52] and r = .35, 95% CI [.15, .53], respectively). Regression analysis indicated that self-reported external gain expectations did not account for the relationship between symptom overreporting and alexithymia. Conclusion: These findings suggest that alexithymia is associated with symptom overreporting independently of self-reported external gain expectations. More broadly, the results raise the possibility that alexithymic traits may compromise the accuracy of symptom reporting itself. If so, this has implications not only for the interpretation of symptom validity tests, but also for the broader use of self-report measures in clinical assessment.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".