The health care system is making ‘too much noise’ to provide family-centred care in neonatal intensive care units: Perspectives of health care providers and hospital administrators
Bibliographic record
Abstract
AIM: To describe the perspectives of health care providers and hospital administrators on their experiences of providing care for infants in Level II neonatal intensive care units and their families. RESEARCH METHODS: We conducted 36 qualitative interviews with neonatal health care providers and hospital administrators and analysed data using a descriptive interpretive approach. SETTING: 10 Level II Neonatal Intensive Care Units in a single, integrated health care system in one Canadian province. FINDINGS: Three major themes emerged: (1) providing family-centred care, (2) working amidst health care system challenges, and (3) recommending improvements to the health care system. The overarching theme was that the health care system was making 'too much noise' for health care providers and hospital administrators to provide family-centred care in ways that would benefit infants and their families. Recommended improvements included: refining staffing models, enhancing professional development, providing tools to deliver consistent care, recognising parental capacity to be involved in care, strengthening continuity of care, supporting families to be with their infant, and designing family-friendly environments. CONCLUSION: When implementing family-centred care initiatives, health care providers and hospital administrators need to consider the complexity of providing care in Level II Neonatal Intensive Care Units, and recognise that health care system changes may be necessary to optimise implementation.
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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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.019 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".