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Record W4240871504 · doi:10.31234/osf.io/v458u

Musical Engagement and Parent-Child Attachment in Families with Young Children During the Covid-19 Pandemic

2020· preprint· en· W4240871504 on OpenAlexaboutno aff
Selena Steinberg, Talia Liu, Miriam Lense

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesNational Institutes of HealthVanderbilt University Medical CenterNational Center for Complementary and Integrative HealthNational Endowment for the Arts
KeywordsPsychologyMusicalDevelopmental psychologyDistressCoronavirus disease 2019 (COVID-19)PandemicMental healthAnxietyClinical psychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

The onset of the Covid-19 pandemic has disrupted the lives of families in the United States and across the world, impacting parent mental health and stress, and in turn, the parent-child relationship. Music is a common parent-child activity and has been found to positively impact relationships, but little is known about music’s role in parent-child interactions during a pandemic. The current study utilized an online questionnaire to assess the use of music in the home of young children and their parents in the USA and Canada during Covid-19 and its relationship with parents’ affective attachment with their child. Musical activity was high for both parents and children. Parents reported using music for both emotion regulation and to socially connect with their children. Parent-child musical engagement was associated with parent-child attachment, controlling for relevant parent variables including parent distress, efficacy, education and parent-child engagement in non-musical activities. These results indicate that music may be an effective tool for building and maintaining parent-child relationships during a period of uncertainty and change.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.002
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 head0.098
GPT teacher head0.376
Teacher spread0.278 · 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 teacher head, not a consensus.

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

Citations6
Published2020
Admission routes1
Has abstractyes

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