Multimodal Interaction between a Mother and Her Twin Preterm Infants (Male and Female) in Maternal Speech and Humming during Kangaroo Care: A Microanalytical Case Study
Bibliographic record
Abstract
The literature reports the benefits of multimodal interaction with the maternal voice for preterm dyads in kangaroo care. Little is known about multimodal interaction and vocal modulation between preterm mother–twin dyads. This study aims to deepen the knowledge about multimodal interaction (maternal touch, mother’s and infants’ vocalizations and infants’ gaze) between a mother and her twin preterm infants (twin 1 [female] and twin 2 [male]) during speech and humming in kangaroo care. A microanalytical case study was carried out using ELAN, PRAAT, and MAXQDA software (Version R20.4.0). Descriptive and comparative analysis was performed using SPSS software (Version V27). We observed: (1) significantly longer humming phrases to twin 2 than to twin 1 (p = 0.002), (2) significantly longer instances of maternal touch in humming than in speech to twin 1 (p = 0.000), (3) a significant increase in the pitch of maternal speech after twin 2 gazed (p = 0.002), and (4) a significant increase of pitch in humming after twin 1 vocalized (p = 0.026). This exploratory study contributes to questioning the role of maternal touch during humming in kangaroo care, as well as the mediating role of the infant’s gender and visual and vocal behavior in the tonal change of humming or speech.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".