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Record W4224242965 · doi:10.2196/34522

Understanding the Cognitive Demands, Skills, and Assessment Approaches for Endotracheal Intubation: Cognitive Task Analysis

2022· article· en· W4224242965 on OpenAlexvenueno aff
Taylor Kunkes, Basiel Makled, Jack Norfleet, Steven D. Schwaitzberg, Lora Cavuoto

Bibliographic record

VenueJMIR Perioperative Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicAirway Management and Intubation Techniques
Canadian institutionsnot available
FundersU.S. Army Combat Capabilities Development Command Soldier Center
KeywordsCompetence (human resources)CognitionTask (project management)Endotracheal intubationTask analysisWorkloadAirway managementTask managementMedical educationPsychologyMedicineComputer scienceIntubationEngineering

Abstract

fetched live from OpenAlex

BACKGROUND: Proper airway management is an essential skill for hospital personnel and rescue services to learn, as it is a priority for the care of patients who are critically ill. It is essential that providers be properly trained and competent in performing endotracheal intubation (ETI), a widely used technique for airway management. Several metrics have been created to measure competence in the ETI procedure. However, there is still a need to improve ETI training and evaluation, including a focus on collaborative research across medical specialties, to establish greater competence-based training and assessments. Training and evaluating ETI should also incorporate modern, evidence-based procedural training methodologies. OBJECTIVE: This study aims to use the cognitive task analysis (CTA) framework to identify the cognitive demands and skills needed to proficiently perform a task, elucidate differences between novice and expert performance, and provide an understanding of the workload associated with a task. The CTA framework was applied to ETI to capture a broad view of task and training requirements from the perspective of multiple medical specialties. METHODS: A CTA interview was developed based on previous research into the tasks and evaluation methods of ETI. A total of 6 experts from across multiple medical specialties were interviewed to capture the cognitive skills required to complete this task. Interviews were coded for main themes, subthemes in each category, and differences among specialties. These findings were compiled into a skills tree to identify the training needs and cognitive requirements of each task. RESULTS: The CTA revealed that consistency in equipment setup and planning, through talk or think-aloud methods, is critical to successfully mastering ETI. These factors allow the providers to avoid errors due to patient characteristics and environmental factors. Variation among specialties derived primarily from the environment in which ETI is performed, subsequent treatment plans, and available resources. Anesthesiology typically represented the most ideal cases with a large potential for training, whereas paramedics faced the greatest number of constraints based on the environment and available equipment. CONCLUSIONS: Although the skills tree cannot perfectly capture the complexity and detail of all potential cases, it provided insight into the nuanced skills and training techniques used to prepare novices for the variability they may find in practice. Importantly, the CTA identified ways in which challenges faced by novices may be overcome and how this training can be applied to future cases. By making these implicit skills and points of variation explicit, they can be better translated into teachable details. These findings are consistent with previous studies looking at developing improved assessment metrics for ETI and expanding upon their work by delving into methods of feedback and strategies to assist novices.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.859
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.138
GPT teacher head0.371
Teacher spread0.234 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

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