Introduction of microsystems in a level 3 neonatal intensive care unit—an interprofessional approach
Bibliographic record
Abstract
BACKGROUND: Growth of neonatal intensive care units in number and size has raised questions towards ability to maintain continuity and quality of care. Structural organization of intensive care units is known as a key element for maintaining the quality of care of these fragile patients. The reconstruction of megaunits of intensive care to smaller care units within a single operational service might help with provision of safe and effective care. METHODS/DESIGN: The clinical team and patient distribution lay out, admission and discharge criteria and interdisciplinary round model was reorganized to follow the microstructure philosophy. A working group met weekly to formulate the implementation planning, to review the adaptation and adjustment process and to ascertain the quality of implementation following the initiation of the microsystem model. DISCUSSION: In depth examination of microsystem model of care in this study, provides systematic evaluation of this model on variable aspects of health care. The individual projects of this trial can be source of solid evidence for guidance of future decisions on optimized model of care for the critically ill newborns. TRIAL REGISTRATION: ClinicalTrial.gov, NCT02912780 . Retrospectively registered on 22 September 2016.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".