Maternal depressive symptoms and early childhood temperament before and during the <scp>COVID</scp> ‐19 pandemic in the United Kingdom
Bibliographic record
Abstract
Abstract The COVID‐19 pandemic is an unexpected and major global event, with the potential to have many and varied impacts on child development. However, the implications of the pandemic for maternal depressive symptoms, early childhood temperament dimensions, and their associations, remain largely unknown. To investigate this, questionnaires were completed by mothers ( N = 175) before and during the pandemic when their child was 10‐ and 16‐months old (Study 1), and by an extended group of mothers with young children (6–48 months; 66 additional mothers) during the first and second national lockdowns in the United Kingdom in 2020 (Study 2). Results indicated that while maternal pandemic‐related stress decreased over the first 6 months of the pandemic, there was an increase in mothers who reported feeling some level of pandemic‐specific depression. Despite this, we did not observe an increase in the severity of global maternal depressive symptoms, or any negative impact of the pandemic on the development of temperament in infancy and early childhood.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".