The Sound of Comfort: Neonatal Health Care Professionals' Perspectives on Music and Other Comfort Measures during Targeted Neonatal Echocardiography
Bibliographic record
Abstract
This study aimed to assess health care professionals' perspectives on how implemented measures impact patient comfort during targeted neonatal echocardiography (TNE).Survey distributed to neonatal health care professionals at the Montreal Children's Hospital neonatal intensive care unit (NICU). Responses were collected for 4 weeks, anonymized, and analyzed using descriptive statistics.Of 110 respondents, most believed that scans in general disturbed infants (71%) by increasing the risk of hypothermia (75%), and lability (67%). Key comfort measures identified were warm gel (85%), bundling (80%), and a focused exam (<30 minutes; 80%). Neoclassical music recordings were valued for their calming effect on the infant (73%), parent (44%), and sonographer (39%). Respondents preferred recorded music over other forms of music delivery (53%).Health care workers generally agree that scans disturb newborns and that implementing comfort measures, such as music and the cost-efficient bundle used in our NICU, may enhance patient comfort. Further objective studies are needed to validate these findings and assess their impact on neonatal care outcomes · NICU staff surveyed on TNE comfort measures.. · Music may calm babies, parents, and workers.. · Warm gel, bundling, and focused exam highly valued.. · Care bundle is cheap and easily implementable..
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".