The Variables That Lead to Severe Action Decisions by the Liaison Committee on Medical Education
Bibliographic record
Abstract
PURPOSE: To identify the variables associated with severe action decisions (SADs) (unspecified accreditation term, warning status, probation status) by the Liaison Committee on Medical Education (LCME) regarding the accreditation status of established MD-granting medical education programs in the United States and Canada. METHOD: The authors reviewed all LCME decisions made on full survey reports between October 2004 and June 2012 to test whether SADs were associated with an insufficient response in the data collection instrument/self-study, chronic noncompliance with one or more accreditation standards, noncompliance with specific standards, and noncompliance with a large number of standards. RESULTS: The LCME issued 103 nonsevere action decisions and 40 SADs. SADs were significantly associated with an insufficient response in the data collection instrument/self-study (odds ratio [OR] = 7.30; 95% confidence interval [CI] = 2.38-22.46); chronic noncompliance with one or more standards (OR = 12.18; 95% CI = 1.91-77.55); noncompliance with standards related to the educational program for the MD degree (ED): ED-8 (OR = 6.73; 95% CI = 2.32-19.47) and ED-33 (OR = 5.40; 95% CI = 1.98-14.76); and noncompliance with a large number of standards (rpb = 0.62; P < .001). CONCLUSIONS: These findings provide insight into the LCME's pattern of decision making. Noncompliance with two standards was strongly associated with SADs: lack of evidence of comparability across instructional sites (ED-8) and the absence of strong central management of the curriculum (ED-33). These results can help medical school staff as they prepare for an LCME full survey visit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.046 | 0.211 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".