Neonatal transport services, a cross-sectional study
Bibliographic record
Abstract
Objective: To understand the process and challenges facing neonatal transport in Canada and to delineate their composition and working.Subjects and methodology: An online questionnaire was sent to all neonatal transport team directors/coordinators in Canada. The questionnaire covered different aspects of transport and was pilot tested prior to finalization. The responses were anonymous to the investigators.Results: All sixteen neonatal transport teams in Canada surveyed. Fifteen teams responded. Dedicated team as a model was adopted by 12 teams (80%). A combined Neonatal/pediatrics team, where the team could be assembled by either neonatal or pediatrics intensive care staff, adopted by two (13%). Team members were cross-trained in about quarter of the teams (four teams out of 15) with respiratory therapists and registered nurses performing each other’s roles. Neonatal Resuscitation Program was mandatory for all teams that responded (15 teams) to become certified as a neonatal transport team member. Nine teams use a central dispatch phone call system.Conclusion: As the first to comprehensively describe the status of neonatal transport in Canada, our study shows that neonatal transport teams have similarities as well as differences. Regionalization and differences in referral practices, geography, provincial laws, and manpower are the main reasons why teams may have their individual variations in policies, protocols, and logistics. Our data can be utilized by health professionals and policy makers to improve neonatal transport logistics within their health care systems resulting in better outcomes of transported neonates.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".