The Relationship Between Alexithymia and Risk for Postpartum Depression
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) is classified as a stressful life experience. The aim of this study was to assess the levels of alexithymia and risk for postpartum depression in women during the COVID-19 pandemic and state-of-emergency lockdown in Serbia. This cross-sectional study included 108 adult (age ≥18 years) postpartum women with children age 12 months or younger. We used the following questionnaires: Edinburgh Post-natal Depression Scale (EPDS), Toronto alexithymia scale (TAS), Hamilton scale for the assessment of anxiety (HAM-A) and depression (HAM-D), and an additional questionnaire constructed for this study. We found that 14.8% of participants had a score of 10 or higher on EPDS, 23.15% had alexithymia, and 31.32% had borderline alexithymia. There was a significant positive correlation of alexithymia with risk for postpartum depression, high-intensity anxiety, and depression in postpartum women with a score of 10 or higher on HAM-D and 18 or higher on HAM-A. Higher rates on TAS were noticed in mothers who were older, single, and dissatisfied with emotional status, with lower educational level, unemployment, and lack of family support. Multiple linear regression analysis including all factors that correlate with higher TAS scores showed that higher scores on HAM-D (beta = 0.75, P < .01) and higher scores on EPDS (beta = 0.69; P < .01) were independent predictors of higher EPDS scores (adjusted R 2 = 0.52, P < .01 for overall model). Alexithymia could develop in response to pandemics, and pandemics and the measures taken to combat a pandemic could be considered as risk factors for postpartum depression. [ Psychiatr Ann . 2021;51(9):431–436.]
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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 | 0.002 |
| 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) | 0.001 | 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".