Alexithymia, Depression and Social Support among Japanese Workers
Bibliographic record
Abstract
BACKGROUND: A number of studies have shown that social support has a direct beneficial effect on well-being and also serves as a buffer to protect people from health problems due to excessive stress. Although preliminary studies report a positive relationship of alexithymia both with depression and reduced social support, there is no study examining whether the beneficial effect of social support on depression differs with the presence of alexithymia. METHODS: A total of 120 workers aged 19-39 completed the 20-item Toronto Alexithymia Scale (TAS-20) to measure alexithymia, the Beck Depression Inventory-II (BDI-II) to evaluate depressive symptomatology, and the Job Content Questionnaire (JCQ) to assess job strain based on Karasek's demand-control-support model. The interrelationship among TAS-20, BDI-II and 3 subscales of JCQ (job demand, control, and support) were examined. RESULTS: A significant association of depression with low support and high alexithymia was observed. Alexithymia was also associated with reduced support. Further, a statistically significant interaction between alexithymia and support in terms of their effect on depression was observed. Nonalexithymic individuals with low support showed a significantly higher depression score than those who received high support, while alexithymics did not differ in their depression score depending on the degree of support. Consistent results were obtained from the logistic regression analysis examining the odds ratio for depression by support by alexithymia; a significantly increased odds ratio for depression associated with low social support was observed only among nonalexithymics. CONCLUSIONS: Alexithymic individuals might be unable to benefit from social support because of their cognitive deficits of emotion.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".