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Cadaveric‐based Airway Management Instructional Videos to Supplement Traditionally Taught Patient Care Skills for Emergency Healthcare Providers

2019· article· en· W3176999000 on OpenAlexaffabout
William Albabish, Gary J. Sullivan, David Wall, Lorraine Jadeski

Bibliographic record

VenueThe FASEB Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicAirway Management and Intubation Techniques
Canadian institutionsFanshawe CollegeUniversity of Guelph
Fundersnot available
KeywordsMedicineHealth careIntervention (counseling)Airway managementEmergency departmentMedical educationIntubationPsychologyNursingSurgery

Abstract

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Effective administration of emergency medical care relies on the knowledge and skills of highly trained healthcare practitioners. As the scope of emergency medical care practice expands in the pre‐hospital and hospital environments, approaches to emergency healthcare education must continue to evolve; comprehensive understanding of human anatomy becomes more important than ever. The University of Guelph (Human Anatomy Program) and Fanshawe College (Schools of Health Science and Public Safety) are collaborating to develop a cadaveric‐based educational resource to facilitate the teaching of techniques and clinical skills associated with intubation procedures: endotracheal tube with stylet (ET‐S), endotracheal tube with bougie introducer (ET‐B), and intubation through a laryngeal mask airway (ET‐LMA). When presented in conjunction with, or following, ‘traditional’ anatomy and patient care procedures, the overall intent is to encourage critical thought relative to health science theory and current professional practice. We tested the efficacy of cadaveric‐based‐digital modules focused on the anatomy and skills associated with intubation procedures on first‐year students enrolled in the Respiratory Therapy Program at Fanshawe College. Participants entered the study with traditional ‘classroom knowledge’ of ET‐S and ET‐B; participants had no prior knowledge of ET‐LMA. Participants' knowledge of relevant anatomy was assessed pre‐ and post‐intervention. For procedures, participants learned and practiced using low fidelity task trainers, with either traditional teaching material (i.e., control group) or audiovisual modules (i.e., experimental group). Following ET procedures, participants' competencies were tested. Pre‐intervention assessment of anatomical knowledge did not differ (0.77 ± 7.73%, p = 0.921), whereas post‐intervention assessment of anatomical knowledge differed between the two groups (24.21 ± 7.73%, p = 0.004). Specifically, when test scores for anatomical knowledge assessment were compared pre‐ and post‐intervention, the experimental group improved their scores by 26.59 ± 6.14% ( p = 0.001); the control did not improve post‐intervention (1.60 ± 3.16%, p = 0.621) (Figure 1). When combined with classroom‐based instruction, cadaveric‐based training enhanced students' intubation skills for ET‐S and ET‐B procedures (ET‐S: 17.86 ± 5.65%, p = 0.004; ET‐B: 12.86 ± 5.83%, p = 0.036). Interestingly, post‐intervention intubation skills did differ between experimental and control groups for the procedure in which students had no prior knowledge, the ET‐LMA procedure (ET‐LMA: 1.96 ± 5.53%, p = 0.76) (Figure 2). Student feedback suggested that cadaveric‐based learning improved their understanding and capacity to visualize anatomy, promoting an enhanced understanding of procedures. Procedural techniques were enhanced only when a cadaveric‐based module was associated with classroom‐based instruction, suggesting that the most beneficial use of this tool is to supplement traditional instructional practices. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.032
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0320.002

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 head0.010
GPT teacher head0.265
Teacher spread0.255 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations0
Published2019
Admission routes2
Has abstractyes

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