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Record W4393041437 · doi:10.3390/children11030373

Maternal Humming during Kangaroo Care: Effects on Preterm Dyads’ Physiological Parameters

2024· article· en· W4393041437 on OpenAlexfundno aff
Maria Eduarda Salgado Carvalho, João Manuel Rosado Miranda Justo

Bibliographic record

VenueChildren · 2024
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsnot available
FundersFundação para a Ciência e a TecnologiaFederation for the Humanities and Social Sciences
KeywordsHumAudiologyHeart rateCardiorespiratory fitnessMedicinePhysiologyInternal medicineBlood pressure

Abstract

fetched live from OpenAlex

Humming is probably more effective than speech for improving mothers’ cardiorespiratory function and infants’ self-regulation. We intend to understand the effects of (1) maternal humming vs. speech on preterm infants’ physiological parameters, (2) maternal humming vs. speech on mothers’ physiological parameters, and (3) humming melodic contours and the process of the lengthening of the final note on preterm infants’ physiological parameters. This study was designed as a single-group repeated measures study, using microanalytical methodology (ELAN software version 4.9.4), with a protocol (silent baseline/speech, humming/silence/humming, or speech/silence) applied to preterm dyads (N = 36). Audio and video observations were recorded. Infants’ and mothers’ heart rates (HR) and O2 saturations were observed once a minute. The proportion of O2 saturation relative to HR (Prop. O2 saturation/HR) was estimated for both partners during the protocol. We found that the infants’ HR mean was significantly lower during humming (p = 0.028), while a significantly higher Prop. O2 saturation/HR ratio was recorded during humming for infants (p = 0.027) and mothers (p = 0.029). The duration of sinusoidal contours, together with the lengthening of the final note, predicts infants’ Prop. O2 saturation/HR ratio. Musical features of humming seem to improve the physiological stability of preterm infants during kangaroo care.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.082
Threshold uncertainty score0.610

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.242
Teacher spread0.233 · 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.

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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