Alexithymia, Risk Factor or Consequence of Work-Related Stress?
Bibliographic record
Abstract
BACKGROUND: Primary alexithymia has been proposed as a trait-like risk factor for various psychiatric disorders. Alternatively, secondary alexithymia has been conceptualized as an inadequate coping reaction to a stressful situation. This study investigated the level and the type of alexithymia associated with occupational stress. METHOD: On 2 occasions, 69 patients with work-related stress and 62 healthy participants completed self-report instruments to measure alexithymia (20-item Toronto Alexithymia Scale), burnout complaints (Maslach Burnout Inventory) and general distress complaints (Depression Anxiety Stress Scales, Checklist Individual Strength). Group differences in alexithymia were analyzed using ANOVAs. The type of alexithymia was investigated by (a) determining absolute and relative stability, (b) exploring state dependence by adjusting alexithymia for burnout and distress complaints and (c) associating recovery of complaints with change in alexithymia. RESULTS: Alexithymia was significantly elevated among patients. In the patient group, absolute stability of two alexithymia dimensions (identifying feelings, describing feelings) and relative stability of one alexithymia dimension (identifying feelings) was lower than in the healthy group. Cross-sectional group differences became small and nonsignificant after adjustment for distress complaints. Among patients, change in alexithymia was moderately associated with symptom recovery. CONCLUSION: Elevated alexithymia among patients with occupational stress is highly state dependent, which indicates the presence of secondary alexithymia.
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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".