Bibliographic record
Abstract
Background and Purpose: Neonatal aeromedical transport is inherently dangerous, (Bouchut, Lancker, Chritin, & Gueugniaud, 2011; Schierholz, 2010), but for Nunavut, Canada, serviced by Keewatin Air, this is the only option to accessing specialized care (McKenzie, 2015). The purpose of this practicum is to support Keewatin Air with a Neonatal Transport Improvement Project (NTIP) to help their staff safely transport neonates. Methods: A needs assessment with Keewatin Air was conducted to determine: 1) neonatal knowledge gaps; 2) relevant primary needs; and 3) resources for continuing education. This writer then consulted with neonatal experts to identify evidence-based recommendations for neonatal transport team training, and how to maintain neonatal safety on transport. Results: Keewatin Air staff identified three primary needs: risk mitigation; improving access to Neonatal Resuscitation Program (NRP); and financial support, and consultation with neonatal experts revealed simulation is the ideal training format. NTIP is presented in two parts: Program Support Presentation, and Simulation Educational Toolkit. The former is a PowerPoint presentation that offers solutions to the identified program needs, and the latter includes the foundations of simulation, educator preparatory material, advice for facilitating effective simulation, and a collection of neonatal simulation scenarios. The simulation toolkit also includes an objective evaluation plan to assess the efficacy of this education. Conclusion: Keewatin Air will now have a toolkit to integrate into their curriculum to improve their medical staff’s neonatal competencies and ultimately neonatal safety during aeromedical transport.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.061 | 0.005 |
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".