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Record W2986286040 · doi:10.1097/ccm.0000000000004071

Online Learning and Residents’ Acquisition of Mechanical Ventilation Knowledge: Sequencing Matters

2019· article· en· W2986286040 on OpenAlexfundno aff
Traci A. Wolbrink, Sandrijn M. van Schaik, David Turner, Steven J. Staffa, Eleanor Keller, Donald L. Boyer, Grace Chong, Jarrod Cross, Sylvia del Castillo, Andrew Feng, R. Stanley Hum, Ebor Jacob James, Amanda Johnson, Sarah Kandil, Martin C. J. Kneyber, Ramachandran Rameshkumar, Amanda Levin, Rakesh Lodha, Muralidharan Jayashree, Anthony Olivero, Felix Oberender, Rahul Panesar, Puneet A. Pooni, Kyle J. Rehder, Shuba Sankaranarayanan, Margaret Scheffler, Rana Sharara‐Chami, Ashley Siems, Rajakumar Padur Sivaraman, Ken Tegtmeyer, Stacey L. Valentine, Florencia Villois, Amelie von Saint André-von Arnim, Margaret K. Winkler, Chris Dede, Jeffrey P. Burns

Bibliographic record

VenueCritical Care Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsnot available
FundersBC Children's HospitalLoma Linda UniversityUniversity of Pennsylvania
KeywordsMedicinePsychological interventionIntervention (counseling)Mechanical ventilationPhysical therapyCrossover studyYoung adultEmergency medicineNursingInternal medicineAlternative medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Rapid advancements in medicine and changing standards in medical education require new, efficient educational strategies. We investigated whether an online intervention could increase residents' knowledge and improve knowledge retention in mechanical ventilation when compared with a clinical rotation and whether the timing of intervention had an impact on overall knowledge gains. DESIGN: A prospective, interventional crossover study conducted from October 2015 to December 2017. SETTING: Multicenter study conducted in 33 PICUs across eight countries. SUBJECTS: Pediatric categorical residents rotating through the PICU for the first time. We allocated 483 residents into two arms based on rotation date to use an online intervention either before or after the clinical rotation. INTERVENTIONS: Residents completed an online virtual mechanical ventilation simulator either before or after a 1-month clinical rotation with a 2-month period between interventions. MEASUREMENTS AND MAIN RESULTS: Performance on case-based, multiple-choice question tests before and after each intervention was used to quantify knowledge gains and knowledge retention. Initial knowledge gains in residents who completed the online intervention (average knowledge gain, 6.9%; SD, 18.2) were noninferior compared with those who completed 1 month of a clinical rotation (average knowledge gain, 6.1%; SD, 18.9; difference, 0.8%; 95% CI, -5.05 to 6.47; p = 0.81). Knowledge retention was greater following completion of the online intervention when compared with the clinical rotation when controlling for time (difference, 7.6%; 95% CI, 0.7-14.5; p = 0.03). When the online intervention was sequenced before (average knowledge gain, 14.6%; SD, 15.4) rather than after (average knowledge gain, 7.0%; SD, 19.1) the clinical rotation, residents had superior overall knowledge acquisition (difference, 7.6%; 95% CI, 2.01-12.97;p = 0.008). CONCLUSIONS: Incorporating an interactive online educational intervention prior to a clinical rotation may offer a strategy to prime learners for the upcoming rotation, augmenting clinical learning in graduate medical education.

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)0.000
metaresearch head score (Gemma)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.039
GPT teacher head0.405
Teacher spread0.366 · 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

Citations18
Published2019
Admission routes1
Has abstractyes

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