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Record W4391787240 · doi:10.32920/25213685

Maternal Parasympathetic Regulation During Dyadic Stress: Associations with Emotional Availability, Maternal Depressive and Anxiety Symptoms, and Infant Distress

2024· preprint· en· W4391787240 on OpenAlexaff
Brittany Jamieson

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsUniversity of GuelphToronto Metropolitan University
Fundersnot available
KeywordsVagal toneStressorAnxietyPsychologyDistressDevelopmental psychologyMaternal sensitivityDepression (economics)Clinical psychologyMedicineAutonomic nervous systemPsychiatryInternal medicineHeart rate

Abstract

fetched live from OpenAlex

Background: Parents, often mothers, are the primary regulators of infant emotion and physiology. Maternal self-regulation is central to the regulation of another, particularly during stress. Appropriate flexibility in the parasympathetic nervous system (indexed via respiratory sinus arrhythmia, RSA) is associated with positive self-regulation. This dissertation aimed to better understand the factors that influence a mother’s parasympathetic regulation during dyadic stress. Method: A community sample of 83 mother-infant dyads participated in two visits as part of a larger longitudinal study. During the first visit, at infant age 6 months, dyads were filmed as they interacted for 30-minutes at home. This interaction was later coded for maternal caregiving behaviour using the Emotional Availability Scales. Data on maternal self-reported symptoms of depression and anxiety were also collected. During the second visit, at 6.5 months, dyads participated in an experimental stressor, the still face procedure, involving three episodes: baseline interaction; a still face episode wherein a mother is instructed to remain unresponsive to the infant; and a reunion episode, wherein the mother and infant re-establish interaction. Infant distress (coded in 1 second intervals) and maternal RSA data were collected. Multilevel models assessed maternal RSA trajectories and their relation to maternal factors and infant distress in the still face and reunion episodes. Results: In the still face episode, maternal depressive symptoms, anxiety symptoms and infant distress interacted to predict maternal RSA. Mothers with fewer symptoms of depression and anxiety showed appropriate RSA withdrawal in the context of infant distress, consistent with an adaptive physiological response. In comparison, mothers with more depressive symptoms and high or low anxiety symptoms had increasing RSA trajectories in this context, suggesting less adequate physiological mobilization. Mothers with fewer depressive symptoms and high anxiety symptoms displayed the steepest RSA withdrawal in this episode, suggesting parasympathetic hyperarousal. In the reunion episode, maternal depressive symptoms and emotional availability interacted to predict maternal RSA trajectories. Mothers with fewer depressive symptoms and greater emotional availability displayed trajectories that were consistent with physiological mobilization at the start of the reunion and recovery towards the end. In comparison, mothers with greater depressive symptoms and less emotional availability displayed limited physiological mobilization at the start of the reunion and less physiological recovery towards the end. Conclusions: Findings illustrate the importance of assessing: (i) physiological regulation dynamically, (ii) maternal mood, anxiety, and caregiving in interaction, and (iii) self-regulation in the context of co-regulation. Further, these results highlight differential parental task demands between the still face episode and the reunion episode. Public health implications and future research are discussed.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.009
GPT teacher head0.251
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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