The effect of alexithymic characteristics on perceived stress and health anxiety during the COVID-19 pandemic
Bibliographic record
Abstract
OBJECTIVE: The anxiety of life that comes with the pandemic process increases the health anxiety and the level of perceived stress. However, there are uncertainties about which individuals are more sensitive. This study aims to investigate the effects of alexithymic characteristics on health anxiety and perceived stress.\n\nMATERIALS AND METHODS: The authors invited the participants to study via social media and e-mail. The data of 793 individuals, aged 18-65, collected over the internet (Google Forms) between November and December 2020 were statistically evaluated. Evaluations were made with the sociodemographic data form, the General Health Questionnaire-12 (GHQ-12), the Health Anxiety Scale (HAS), the Toronto Alexithymia Scale (TAS-20), and the Perceived Stress Scale (PSS).\n\nRESULTS: In mediation analyzes between TAS subscales and HAS, Difficulty in Identifying Feelings (DIF) most strongly predicted HAS (B=0.469, p<0.001) and indirectly affected HAS only through GHQ (CS: 0.08, B=0.108, SE:0.021, CI: 0.070, 0.153). However, both PSS (CS: 0.0128, B=0.084, SE:0.027, CI: 0.032, 0.139) and GHQ (CS: 0.02, B=0.139, SE:0.028, CI: 0.090, 0.198) played a mediating role between Difficulty Describing Feelings (DDF) and HAS.\n\nCONCLUSIONS: The present study suggests that individuals with alexithymic features are more sensitive to stress during periods of health-related increased stress, such as pandemics, and that individuals with alexithymic features should be given priority in psychotherapeutic interventions.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".