Comparative Analysis of Junior and Senior Clinician Educator Evaluation of Relevant Articles Within Medical Education
Bibliographic record
Abstract
Introduction It may be difficult for junior clinician educators (JCEs) to get a grasp of pertinent literature and determine which are most relevant to their learning, due to limited experience and lack of formalized system to rank all available resources with respect to their value for JCEs. Our study aimed to identify whether senior clinician educators (SCEs) and JCEs differ in their selection of what they perceive as key medical education articles. Methods As a part of the Academic Life in Emergency Medicine (ALiEM) Faculty Incubator program, we developed a series of primer articles for JCEs by identifying and discussing key articles within specific medical education arenas, which were designed to enhance the reader's educational growth. Each set of articles within the primer series were selected based on data collected from JCEs and SCEs, who ranked the specific articles with respect to their perceived relevancy to the JCEs. ANOVA analysis was performed for each of the series to determine whether there was a statistically significant difference between JCE and SCE rating of articles. Results Two-hundred-and-sixteen total articles were evaluated within the nine primer topics. No statistically significant difference was found between the rankings of papers by JCEs and SCEs (effect size: 0.06; 95% CI: -0.27 to 0.40). However, a subgroup analysis of the data found that three of the nine primers showed statistically significant divergence based on seniority (p < 0.05). Conclusions Based on the data, the involvement of JCEs in the consensus-building process was important in identifying divergence in views between JCEs and SCEs in one-third of cases. Our findings suggest that it is important to involve JCEs in selecting articles that are worthwhile for their learning, since SCEs may not fully understand their needs.
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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 | 0.050 | 0.302 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.019 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".