Prevalence and Relationships between Alexithymia, Anhedonia, Depression and Anxiety during the Belgian COVID-19 Pandemic Lockdown
Bibliographic record
Abstract
Alexithymia and anhedonia are associated with psychiatric disorders, such as depression and anxiety. The COVID-19 pandemic lead to a significant deterioration in the mental health of the population. It is therefore important to examine the effects of lockdown on alexithymia and anhedonia and their relationships with anxiety and depression. We compared the scores and characteristics of 286 patients divided into two groups: one before lockdown (group 1, N = 127), the other during the progressive lockdown release (group 2, N = 159). The groups were homogeneous in terms of age, sex ratio, socio-professional categories, and somatic and psychiatric comorbidities. The groups were compared on the Toronto Alexithymia Scale (TAS-20) measuring alexithymia, the Beck Depression Inventory (BDI-II) measuring depression, the anhedonia subscale of the BDI-II measuring state-anhedonia and the State Trait Anxiety Inventory (STAI) measuring state and trait anxiety. The ratio of alexithymic subjects in group 1 is 22.83% to 33.33% in group 2 (p-value = 0.034). This suggests a significant increase in the number of alexithymic patients after lockdown. We did not observe any difference in the proportion of depressed and anxious subjects before or after lockdown. Among the different scales, higher scores were only found on the cognitive factor of alexithymia on group 2 comparatively to group 1. This study indicates an increase in the proportion of alexithymic subjects following lockdown. Unexpectedly, this was unrelated to depression, anxiety or anhedonia levels, which remained stable. Further studies are needed to confirm this result and to evaluate precisely which factors related to the lockdown context are responsible for such an increase.
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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 | 0.001 |
| 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) | 0.001 | 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".