Supplementary Material for: Unit-Level Variations in Healthcare Professionals’ Availability for Preterm Neonates <29 Weeks’ Gestation: An International Survey
Bibliographic record
Abstract
Introduction: The availability of and variability in healthcare professionals in neonatal units in different countries has not been well characterized. Our objective was to identify variations in the healthcare professionals for preterm neonates in 10 national or regional neonatal networks participating in the International Network for Evaluating Outcomes (iNeo) of neonates. Method: Online, pre-piloted questionnaires about the availability of healthcare professionals were sent to the directors of 390 tertiary neonatal units in 10 international networks: Australia/New Zealand, Canada, Finland, Illinois, Israel, Japan, Spain, Sweden, Switzerland, and Tuscany. Results: Overall, 325 of 390 units (83%) responded. About half of the units (48%; 156/325) cared for 11–30 neonates/day and had team-based (43%; 138/325) care models. Neonatologists were present 24 h a day in 59% of the units (191/325), junior doctors in 60% (194/325), and nurse practitioners in 36% (116/325). A nurse-to-patient ratio of 1:1 for infants who are unstable and require complex care was used in 52% of the units (170/325), whereas a ratio of 1:1 or 1:2 for neonates requiring multisystem support was available in 59% (192/325) of the units. Availability of a respiratory therapist (15%, 49/325), pharmacist (40%, 130/325), dietitian (34%, 112/325), social worker (81%, 263/325), lactation consultant (45%, 146/325), parent buddy (6%, 19/325), or parents’ resource personnel (11%, 34/325) were widely variable between units. Conclusions: We identified variability in the availability and organization of the healthcare professionals between and within countries for the care of extremely preterm neonates. Further research is needed to associate healthcare workers’ availability and outcomes.
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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 | 0.001 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.567 | 0.072 |
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".