Learning Echocardiography in the Workplace: A Cognitive Load Perspective
Bibliographic record
Abstract
PURPOSE: Although workplace learning environments provide authentic tasks to promote learning, elements of clinical settings may distract trainees and impede learning. The characteristics of workplace learning environments that require optimization are ill-defined. Applying principles of cognitive load theory (CLT) to optimize learning environments by managing intrinsic load (complexity of the task matched to learner knowledge and skill), minimizing extraneous load (any aspect that is not part of task completion), and increasing germane load (processing for storage in long-term memory) could be advantageous. The authors explored trainee perceptions of characteristics that helped or impaired learning from a cognitive load perspective. Echocardiography interpretation was used as a model. METHOD: The authors conducted semistructured interviews between December 2018 and March 2019 with a purposeful sample of 10 cardiology trainees at the University of California, San Francisco, School of Medicine until thematic sufficiency was achieved. Participants represented a range of training levels (3 fourth-year trainees, 2 fifth-year trainees, 3 sixth-year trainees, and 2 advanced echocardiography fellows) and career aspirations (4 desired careers in imaging). Two independent coders analyzed interview transcripts using template analysis. Codes were mapped to CLT subcomponents. RESULTS: Trainees selected their own echocardiograms to interpret; if trainees' skill levels and the complexity of the selected echocardiograms were mismatched, excess intrinsic load could result. Needing to look up information essential for task completion, interruptions, reporting software, and time pressures were characteristics that contributed to extraneous load. Characteristics that related to increasing germane load included the shared physical space (facilitating reading echocardiograms with attendings and just-in-time guidance from near peers) and the availability of final reports to obtain feedback independent of teachers. CONCLUSIONS: As interpreted from a cognitive load perspective, findings highlight characteristics of workplace learning environments that could be optimized to improve learning. The findings have direct application to redesigning these learning environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".