Attitudes and Experiences Among Swedish NICU Nurses Regarding Skin-to-Skin Care of Infants Born at 22-23 Weeks of Gestation
Bibliographic record
Abstract
BACKGROUND: Skin-to-skin care (SSC) is an evidence-based care method for preterm-born infants. Staff may hesitate to initiate early SSC, the first or second week of life, for the most extremely preterm infants; 1 reason could be nurses' attitudes. PURPOSE: The aim was to investigate the attitudes and experiences of neonatal nurses regarding early SSC for the most extremely preterm infants, born at gestational week (GW) 22-23, using an exploratory and descriptive approach. METHODS: A web-based survey was sent to all nurses across the 6 Swedish units routinely caring for the most extremely preterm infants. Descriptive statistics and qualitative content analysis were used to analyze the data. RESULTS: The response rate was 258 out of 547 (47%). This study reveals a spectrum of attitudes and experiences among nurses, ranging from viewing early SSC as an integral aspect of infant care to deeming it very challenging or simply not feasible. The outcomes are presented in 2 categories, describing both facilitators and challenges associated with early SSC. Noteworthy, facilitators include well-implemented guidelines that support early SSC. Conversely, challenges encompass knowledge gaps and the scarcity of scientific evidence demonstrating the safety of all SSC, including early, for the most extremely preterm infants. IMPLICATIONS FOR PRACTICE AND RESEARCH: This study underscores the importance of spreading knowledge, and has clinical adapted guidelines about the practical aspects of SSC for infants born at 22-23 GW. Future research is required to clarify safety aspects pertaining to SSC for the most extremely preterm infants.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".