MétaCan
Menu
Back to cohort
Record W4405566558 · doi:10.1186/s13049-024-01296-w

Pre-alerts from critical care ambulances to trauma centers: a quantitative survey of trauma team leaders in Ontario, Canada

2024· article· en· W4405566558 on OpenAlexaffabout
Tara Williams, Brodie Nolan, Melissa McGowan, Tania Johnston, Sonja Maria, Johannes von Vopelius‐Feldt

Bibliographic record

VenueScandinavian Journal of Trauma Resuscitation and Emergency Medicine · 2024
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsUniversity of TorontoProcess Research Ortech (Canada)St. Michael's Hospital
FundersCharles Sturt University
KeywordsLikert scaleMedicineMajor traumaMedical emergencyPsychology

Abstract

fetched live from OpenAlex

INTRODUCTION: Pre-alerts from paramedics to trauma centers are important for ensuring the highest quality of trauma care. Despite this, there is a paucity of data to support best practices in trauma pre-alert notifications. Within the trauma system of Ontario, Canada, the provincial critical care transport organization, Ornge, provides pre-alerts to major trauma centers, but standardization is currently lacking. This study examined the satisfaction of trauma team leaders' (TTLs) satisfaction with current trauma pre-alerts and their preferences for logistics, content, and structure. METHODS: This was a quantitative survey of TTLs at adult and pediatric trauma centers across Ontario, Canada. Recruitment was through email to trauma directors, with follow-up efforts to target low-response sites to achieve good geographical representation. The survey was completed online and contained a combination of single or multiple-choice questions, Likert scales and free text options. RESULTS: In total, 79 TTLs from adult and pediatric lead trauma centers across Ontario responded to the survey, which took place over a 120-day period. The survey achieved good geographical representation. Given the current processes, TTLs describe moderate satisfaction with room for improvement (median score 3, IQR 3-4 on a 5-point Likert scale). Their overall preference was for timely and direct communication, with some concerns about multiple channels of communication around logistics. Most TTLs agreed on the important and less important content details found in common standardized framework tools. For structure, 28/79 TTLs strongly preferred the cognitive aid ATMIST, 13/79 preferred IMIST-AMBO, and 8/79 preferred MIST or SBAR as the most useful. CONCLUSIONS: There is room for improvement through standardizing communication and streamlined pre-alert channels. Some disagreements exist between TTLs, particularly regarding logistics. Further research should examine TTL satisfaction after implementing the change in the pre-alert notification framework, which can address localized issues through stakeholder meetings with individual TTLs.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.155
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.086
GPT teacher head0.373
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueScandinavian Journal of Trauma Resuscitation and Emergency MedicineSame topicTrauma and Emergency Care StudiesFrench-language works237,207