Maternal mood and concordant maternal and infant salivary cortisol during heel lance while in kangaroo care
Bibliographic record
Abstract
BACKGROUND: Maternal kangaroo care (MKC) is a naturalistic intervention that alleviates neonatal pain, and mothers are assumed to play a stress regulatory role in MKC. Yet, no MKC infant pain study has examined relationship between maternal and infant stress reactivity concurrently, or whether post-partum depression and/or anxiety (PPDA) alters maternal and neonatal stress response and the regulatory effects of MKC. OBJECTIVES: To examine the concordance of salivary cortisol reactivity between 42 mothers and their stable preterm infants during routine infant heel lance (HL) while in MKC and to compare salivary cortisol between groups of mothers with and without PPDA and their infants. METHODS: Maternal and infant salivary cortisol samples were collected pre-HL and 20 min post-HL with two additional maternal samples at night and in the morning. Mothers and infants were allocated to with PPDA versus without PPDA study groups on the basis of maternal post-natal mental health assessment scores. RESULTS: Higher mothers' cortisol pre-HL was weakly associated with higher infants' salivary cortisol in response to the HL procedure. Maternal depression and/or anxiety were not associated with infants' cortisol. During HL, both groups of mothers and infants showed no change in salivary cortisol. CONCLUSIONS: Concordance between mother and infant salivary cortisol supports the maternal stress regulatory role in MKC. MKC may have stress regulatory benefits for mothers and their preterm infants during HL independent of PPDA. Future MKC studies that target mothers with altered mood will help to build on these findings.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".