Making the Mc<scp>MOST</scp> out of Milestones and Objectives: Reimagining Standard Setting Using the McMaster Milestones and Objectives Stratification Technique
Bibliographic record
Abstract
BACKGROUND: As we enter the era of milestones and competency-based medical education (CBME), there is an increasing need to examine the procedures for stratifying objectives into levels of achievement. Most techniques used to date (e.g., Delphi surveys) involve some sort of consensus-based process, essentially crowd-sourcing wisdom of multiple educators to set anticipated milestones. In most graduate education settings, however, many simply use the judgment of one or two educators when setting educational objectives. Meanwhile, standard-setting procedures have been historically used in medical education for setting cut-points to determine levels of acceptable performance and do so in a more robust manner. OBJECTIVES: Inspired by these standard-setting procedures, the authors sought to develop a new way to stratify objectives into three relative levels of achievement (junior [ACGME level 1], intermediate [ACGME level 2-3], senior [ACGME level 4]). METHODS: The authors describe a novel, stepwise method that is composed of four steps. There are four steps to the McMOST procedure: 1) sorting objectives with a group of experienced teachers, 2) factor analysis to group preferences, 3) labeling components and reorganizing groupings, and 4) confirmation and final review by educational and content experts. RESULTS: Using McMOST method resulted in a change of placement for 15 of 34 (44%) of the milestones and improved agreement in two of three levels (intermediate from intraclass correlation of 0.56 to 0.80; senior from 0.69 to 0.79). CONCLUSIONS: The authors describe a novel protocol for stratifying objectives that may be useful to stratify and sort competencies into various levels of achievement (e.g., milestones) in this era of CBME.
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".