MétaCan
Menu
Back to cohort
Record W4206669488 · doi:10.1007/s43678-021-00248-y

Experiences of healthcare providers with a novel emergency response intubation team during COVID-19

2022· article· en· W4206669488 on OpenAlexaffabout
Daniel Dongjoo Lee, Matthew Hacker Teper, Lucas B. Chartier, Stephanie Crump, Martín Ma, Matteo Parotto, Pauline Perri, Ki Jinn Chin, Konika Nirmalanathan, Sam Sabbah, Ahmed Taher

Bibliographic record

VenueCanadian Journal of Emergency Medicine · 2022
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsMedicineThematic analysisEmergency departmentHealth careIntubationNursingScope of practiceMedical emergencyRapid response teamGrounded theoryQualitative researchAnesthesia

Abstract

fetched live from OpenAlex

OBJECTIVES: In the early stages of the COVID-19 pandemic, there were significant concerns about the infectious risks of intubation to healthcare providers. In response, a dedicated emergency response intubation team (ERIT) consisting of anesthesiologists and allied health providers was instituted for our emergency department (ED). Given the high-risk nature of intubations and the new interprofessional team dynamics, we sought to assess health-care provider experiences and potential areas of improvement. METHODS: Surveys were distributed to healthcare providers at the University Health Network, a quaternary healthcare centre in Toronto, Canada, which includes two urban EDs seeing over 128,000 patients per year. Participants included ED physicians and nurses, anesthesiologists, anesthesia assistants, and operating room nurses. The survey included free-text questions. Responses underwent thematic analysis using grounded theory and were independently coded by two authors to generate descriptive themes. Discrepancies were resolved with a third author. Descriptive themes were distilled through an inductive, iterative process until fewer main themes emerged. RESULTS: A total of 178 surveys were collected (68.2% response rate). Of these, 123 (69%) participated in one or more ERIT activations. Positive aspects included increased numbers of staff to assist, increased intubation expertise, improved safety, and good team dynamics within the ERIT team. Challenges included a loss of scope (primarily ED physicians and nurses) and unfamiliar workflows, perceived delays to ERIT team arrival or patient intubation, role confusion, handover concerns, and communication challenges between ED and ERIT teams. Perceived opportunities for improvement included interprofessional training, developing clear guidelines on activation, inter-team role clarification, and guidelines on handover processes post-intubation. CONCLUSIONS: Healthcare providers perceived that a novel interprofessional collaboration for intubations of COVID-19 patients presented both benefits and challenges. Opportunities for improvement centred around interprofessional training, shared decision making between teams, and structured handoff processes.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0190.000

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 head0.139
GPT teacher head0.438
Teacher spread0.299 · 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 designQualitative
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

Citations1
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Emergency MedicineSame topicDisaster Response and ManagementFrench-language works237,207