Psychological pain and depression: it’s hard to speak when it hurts
Bibliographic record
Abstract
OBJECTIVE: To investigate the neuropsychological features of depressed patients reporting high level of psychological pain. METHODS: Sixty-two inpatients were included and divided into two groups according to the level of psychological pain assessed by a Likert scale. Cognitive abilities were assessed using the Trail Making Test, the Stroop test, and Verbal Fluency Test (semantic and phonemic verbal fluency). Univariate and multivariate analyses were performed to determine neuropsychological factors associated with a high level of psychological pain. RESULTS: = 0.009), but not in women, even after controlling for confounding factors (age, level of depression, anxiety). Groups did not differ on the Trail Making Test, the Stroop test, or the semantic verbal fluency measure. CONCLUSION: Psychological pain is a specific clinical entity that should be considered to be more significant than just a symptom of depression. High level of psychological pain appears to be associated with a deficit of phonemic verbal fluency in depressed men. This finding could help to target psychotherapeutic treatments and improve screening.Key pointsPatients with high psychological pain do not differ on the Trail Making Test, the Stroop Test or the Sematic Verbal Fluency Measure to patients with low psychological painHigh psychological pain is associated with a deficit in phonemic verbal fluency in depressed menFuture research should aim to clarify gender differences in psychological pain in participants with and without major depressive disorder, as well as explore the complex relationship between cognition and the different forms of pain (psychological, physical and psychosomatic).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".