How to Use Structural Equation Modeling in Medical Education Research: A Brief Guide
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Structural equationmodeling (SEM) is a family of statistical techniques used for the analysis of multivariate data to measure latent variables and their interrelationships. SEM has potential to advance theory and research in medical education. PURPOSE: The purpose of this article is to introduce SEM to medical education researchers and provide procedural information for applying SEM. METHODS: We outline the basic tenets of SEM, principles of model creation, identification, estimation, and model fit to data, and the use of SEM in medical education research. RESULTS: Although it is a powerful statistical research tool, SEM has had only limited use in medical education research. We explicate a five-step procedure for applying SEM to research problems and summarize an example of SEM to test a hypothetical model. CONCLUSIONS: Notwithstanding some pitfalls, SEM does provide promise for testing complex, integrated theoretical models and advance research in medical education.
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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 | 0.130 | 0.773 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it