Caring for late preterm infants: public health nurses’ experiences
Bibliographic record
Abstract
BACKGROUND: Public health nurses (PHNs) care for and support late preterm infants (LPIs) and their families when they go home from the hospital. PHNs require evidence-informed guidelines to ensure appropriate and consistent care. The objective of this research study is to capture the lived experience of PHNs caring for LPIs in the community as a first step to improving the quality of care for LPIs and support for their parents. METHODS: = 10) to understand PHN perceptions of caring for LPIs and challenges in meeting the needs of families within the community. Interpretative thematic analysis revealed PHN perceptions of caring for LPIs and challenges in meeting the needs of families within the community. RESULTS: Four themes emerged from the data. First, PHNs expressed challenges with meeting the physiological needs of LPIs and gave voice to the resulting strain this causes for parents. Second, nurses conveyed that parents require more anticipatory guidance about the special demands associated with feeding LPIs. Third, PHNs relayed that parents sometimes receive inconsistent advice from different providers. Lastly, PHNs acknowledged that due to lack of resources, families sometimes did not receive the full scope of evidence informed care required by fragile, immature infants. CONCLUSION: The care of LPIs by PHNs would benefit from more research about the needs of these infants and their families. Efforts to improve quality of care should focus on: evidence-informed guidelines, consistent care pathways, coordination of follow up care and financial resources, to provide physical, emotional, informational support that families require once they leave the hospital. More research on meeting the challenges of caring for LPIs and their families would provide direction for the competencies PHNs require to improve the quality of care in the community.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| 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".