Mobile Apps for Teaching Intubation: Scoping Review and Critical Analysis in eLearning
Bibliographic record
Abstract
BACKGROUND: Airway management is a core skill in anesthesia ensuring adequate oxygenation and delivery of inhalational agents for the patient. OBJECTIVE: The goals of this study were to critically evaluate the quality of airway management apps and target revised Bloom's Taxonomy cognitive levels. METHODS: An electronic search using the keywords "airway" and "airway management" was conducted in May 2015 across the App Store, Google Play, BlackBerry World, and Windows Store. Apps were included in the study if their content was related to airway management. App content and characteristics were extracted into a standard form and evaluated. RESULTS: A total of 65 apps met the inclusion criteria, and 73% (47/65) of apps were developed by companies or industry. Anesthesiology trainees were the target audience in only 20% (13/65) of apps. Bag mask ventilation and laryngeal mask airways were covered in only 20% (13/65) of apps. Only 2 apps were supported in the scientific literature. For Bloom's Taxonomy, 37% (24/65) of apps targeted knowledge, 5% (3/65) comprehension, 22% (14/65) application, 28% (18/65) analysis, 9% (6/65) evaluation, and 0% synthesis. Multivariate analysis identified cost of apps, size of apps (MB), and apps targeting trainees and paramedics to be associated with higher levels of cognitive processing of revised Bloom's Taxonomy. CONCLUSIONS: Apps developed for teaching intubation target lower levels of cognitive processing and are largely not validated by research. Cost, app size, and targeted user are associated with higher cognitive levels. Trainees and all users should be aware of the paucity of the published evidence behind the efficacy of some of these apps.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 teacher head, 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".