Maternal depression and children’s false belief understanding
Bibliographic record
Abstract
Abstract Children of depressed mothers show substantial social impairment, which increases their risk for developing depression. Theory of mind understanding forms the basis of social functioning, and is impaired in children of currently depressed mothers. Models of risk emphasize that a history of any maternal depression confers risk to later psychopathology. Therefore, we tested a novel model of the impact of lifetime maternal depression on children’s false belief understanding that accounts for three primary factors that scaffold this understanding: maternal mental state talk, and children’s executive functioning and language abilities. Children aged 41–48 months with a maternal lifetime history of major depressive disorder (MDD; n = 19) performed significantly more poorly on the false belief battery compared to those without (n = 44). Further, lower levels of mental state talk, child executive functioning, and child language ability were significantly associated with poorer false belief scores. However, the relation between maternal MDD and children’s false belief performance was not mediated by any of these factors. These results indicate that maternal depression predicts poorer false belief understanding independently of other crucial scaffolding variables, and may be a social cognitive mechanism underlying the intergenerational transmission of depression.
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 | 0.001 | 0.009 |
| 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) | 0.002 | 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".