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Record W4410832714 · doi:10.1111/cdev.14246

Ecological Momentary Assessment Reveals Causal Effects of Music Enrichment on Infant Mood

2025· article· en· W4410832714 on OpenAlexaff
Eun Cho, Lidya Yurdum, Ekanem Ebinne, Courtney B. Hilton, Estelle Lai, Mila Bertolo, Brooke Milosh, Haran Sened, Diana Tamir, Samuel A. Mehr

Bibliographic record

VenueChild Development · 2025
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsMcGill UniversityCentre for Research on Brain Language and Music
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentPrinceton UniversityOffice of Extramural Research, National Institutes of HealthNational Institutes of HealthUniversity of AucklandRoyal Society Te Apārangi
KeywordsPsychologyMoodSingingDevelopmental psychologyPsychological interventionIntervention (counseling)Randomized controlled trialClinical psychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

Abstract Music appears universally in human infancy with self-evident effects: as many parents know intuitively, infants love to be sung to. The long-term effects of parental singing remain unclear, however. In an offset-design exploratory 10-week randomized trial conducted in 2023 (110 families of young infants, Mage = 3.67 months, 53% female, 73% White), the study manipulated the frequency of infant-directed singing via a music enrichment intervention. Results, measured by smartphone-based ecological momentary assessment (EMA), show that infant-directed singing causes general post-intervention improvements to infant mood, but not to caregiver mood. The findings show the feasibility of longitudinal EMA (retention: 92%; EMA response rate: 74%) of infants and the potential of longer-term and higher-intensity music enrichment interventions to improve health in infancy.

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)0.003
metaresearch head score (Gemma)0.009
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 score0.003
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.291
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 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

Citations2
Published2025
Admission routes1
Has abstractyes

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