Video-Coded Maternal Behaviours and Stress Reactivity in Preschoolers of Mothers with Depression
Bibliographic record
Abstract
Parents play an important role in supporting their children’s social-emotional development and well-being. Social buffering theory suggests that positive parent-child relationships are associated to children’s ability to cope with acute stress. One method utilized to measure parent-child relationships is through observed video-coded interactions, but in the context of acute stress, there is an identified gap in standardized video coding systems. We created a video coding scheme to capture maternal behaviours associated with children’s stress reactivity and recovery in a sample of mothers with clinical depression and their preschool aged children (N = 40). Mother-child dyads participated in a baseline assessment of a larger clinical trial study via online videoconferencing platform. Children partook in an acute stressor task alongside salivary cortisol and heart rate measurements. Video recordings of maternal behaviours were collected both during and after the acute stressor task. Transcriptions of maternal behaviours were recorded to inform the microanalytic coding scheme development. These transcriptions were consolidated into codes based on established systems and clinical theory. Partial construct validity of the video coding scheme was found when comparing the observed maternal behaviours with a standardized questionnaire of parenting behaviour. Results indicate that observed global maternal involvement during the online stressor task produced a blunting effect on children’s stress reactivity. However, no associations between mothers’ parenting behaviours after the stressor and children’s stress physiology were found. Results may inform parenting interventions aimed at supporting children’s well-being.
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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 | 0.001 | 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".