Feasibility of an Online Acute Stressor in Preschool Children of Mothers with Depression
Bibliographic record
Abstract
Maternal depression is a risk factor for future mental health problems in offspring, with stress-system function as a candidate vulnerability factor. Here we present initial validation of an online matching-task paradigm in young children exposed to maternal depression (N=40), a first in stressor-paradigm research for this age group. Investigations of stress-system reactivity that can be conducted online are an innovative assessment approach, accelerated by the COVID-19 pandemic. Results indicate high feasibility, with ~80% success across measures, similar-to or better-than in-person success rates in young children. Overall, the online matching task elicited significant HR but not cortisol reactivity. Individual differences in child mental health symptoms were a moderator of reactivity to the stressor such that children with lower, but not higher, behavioural problems exhibited the expected pattern of cortisol reactivity to the online matching task. Results are aligned with allostatic load models, which suggest down-regulation of stress-system reactivity as a result of experiencing adversity and mental health vulnerability. Consistent with in-person research, this suggests an early phenotype for the emergence of behaviour problems may be linked to altered stress-system reactivity. Results hold potential clinical implications for intervention development and the future of online stress-system research.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| 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".