Pandemic Era Maternal Alexithymia and Burnout as Mediated by Self-Efficacy and Resilience
Bibliographic record
Abstract
Abstract Parenting is considered a complex and stressful activity that is associated with the development of parental burnout, especially in the COVID -19 pandemic where mental health problems have a huge impact on individual lives and the division of family roles due to frequent closures. The aim of this study was to investigate whether various psychological characteristics such as alexithymia, resilience, and self-efficacy particularly influence the extent of parental burnout in mothers. For the study, 110 aged women qualified. Only mothers who had full-time jobs and worked from home were invited to participate in the study. Parental burnout was measured using the Parental Burnout Assessment. Level of alexithymia was measured with the Toronto Alexithymia Scale -20. Overall level of resilience as a personality trait was assessed with the Resilience Measurement Scale SPP -25. Beliefs about efficacy in dealing with difficult situations and obstacles were examined with the Generalized Self-Efficacy Scale. The results show that alexithyms had significantly higher levels of burnout than non-alexithyms on the first and second measures. In addition, a significant increase in burnout levels over time was found in the alexithymic group. Alexithymia was a strong predictor of parental burnout and tends to predict a decrease in perceived self-efficacy, which in turn predicts an increase in parental burnout. Finally, alexithymia predicts increased parental burnout through lower psychological resilience. Parents with high levels of parental burnout feel overwhelmed by the stresses associated with their parenting role and often express doubts about their ability to be competent parents.
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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 | 0.001 |
| 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".