Beyond the Task: Developing a Tool to Measure Workplace Characteristics That Affect Cognitive Load and Learning
Bibliographic record
Abstract
PURPOSE: Educators lack tools to measure the workplace characteristics that learners perceive to affect learning. Without a tool that encompasses the social, organizational, and physical components of workplace learning environments (WLEs), it is challenging to identify and improve problematic workplace characteristics. Using echocardiography WLE, this study developed a tool to measure workplace characteristics that cardiology fellows perceive to affect learning. METHOD: The Workplace-Cognitive Load Tool (W-CLT) was developed, which encompasses 17 items to measure workplace characteristics that could affect perceived cognitive load and learning. Exploratory factor analysis was used to identify the most parsimonious structure. A total of 646 cardiology subspeciality fellows were recruited from 60 cardiology fellowship programs to complete the survey between November 2020 and February 2021. Validity evidence was collected, guided by the unified model of validity. RESULTS: A total of 308 fellows (response rate, 49%) participated in the survey. The most parsimonious structure included 4 factors: (1) workplace-task, (2) workplace-environment, (3) workplace-orientation, and (4) workplace-teaching and feedback. All factors had high reliability (Cronbach α = 0.92, 0.92, 0.96, and 0.94, respectively). Social, organizational, and physical components of WLEs were represented in the items. Workplace-teaching and feedback had moderate negative correlations with workplace-environment ( r = -0.41, P < .001) and workplace-orientation ( r = -0.36, P < .001). A moderate positive correlation was found between workplace-task and workplace-teaching and feedback ( r = 0.42, P < .001). Workplace-task had weak negative correlations with workplace-environment ( r = -0.22, P < .001) and workplace-orientation ( r = -0.23, P < .001). CONCLUSIONS: The W-CLT measures workplace characteristics that cardiology fellows perceive to affect their learning. The presence of social, organizational, and physical components emphasizes how workplace characteristics can enhance or impede learning. The W-CLT provides a foundation to explore how learning can be optimized in other WLEs.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".