Structured Referral Call Handling Process Improves Neonatal Transport Dispatch Times
Bibliographic record
Abstract
OBJECTIVE: In 2019 the Southern Alberta Neonatal Transport Service adopted a transport call handling process change to expedite transport team mobilization. This study compares the impact of this change on neonatal transport decision to dispatch and mobilization times. STUDY DESIGN: This retrospective cohort study was conducted using a historical cohort of neonates referred for transportation between January 2017 and December 2021. The "dispatch time" (DT) was the time from the start of consultation to the time a decision to dispatch the transport team was made, whereas "mobilization time" (MT) referred to the time from start of consultation to the time the team departed the home base. In 2019, a DT target of <3 minutes was implemented to meet a target MT of <15 and <30 minutes for emergent and urgent high-risk transport referral calls, respectively. In 2021 use of the "Situation" component of the SBAR (Situation, Background, Assessment, Recommendation) communication tool was introduced with the transport team asking five questions to determine need for mobilization. Data between 2017 and 2018 represented the preintervention period, 2019, the "washout" period for implementation, and 2020 to 2021, the postintervention period. Data were analyzed to determine trends in DT and MT. RESULTS: < 0.001). CONCLUSION: Introduction of a time-sensitive referral call handling process improved dispatch and mobilization time of the neonatal transport team. KEY POINTS: · Time-sensitive triaging of neonatal transport referrals improves dispatch and mobilization time.. · A structured referral call handling process improves the efficiency of neonatal transport decision-making.. · Dedicated neonatal transport vehicles are likely to improve neonatal transport mobilization time..
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.005 | 0.001 |
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".