Measuring the continuous quality improvement orientation of medical education programs
Bibliographic record
Abstract
PURPOSE: There is a growing interest in applying continuous quality improvement (CQI) methodologies and tools to medical education contexts. One such tool, the "Are We Making Progress" questionnaire from the Malcolm Baldrige National Quality Award framework, adequately captures the dimensions critical for performance excellence and allows organizations to assess their performance and identify areas for improvement. Its results have been widely validated in business, education, and health care and might be applicable in medical education contexts. The measurement properties of the questionnaire data were analyzed using Rasch modeling to determine if validity evidence, based on Messick's framework, supports the interpretation of results in medical education contexts. Rasch modeling was performed since the questionnaire uses Likert-type scales whose estimates might not be amenable to parametric statistical analyses. DESIGN/METHODOLOGY/APPROACH: Leaders and teachers at 16 of the 17 Canadian medical schools were invited in 2015-2016 to complete the 40-item questionnaire. Data were analyzed using the ConQuest Rasch calibration program, rating scale model. FINDINGS: 491 faculty members from 11 (69 percent) schools participated. A seven-dimensional, four-point response scale model better fit the data. Overall data fit to model requirements supported the use of person measures with parametric statistics. The structural, content, generalizability, and substantive validity evidence supported the interpretation of results in medical education contexts. ORIGINALITY/VALUE: For the first time, the Baldrige questionnaire results were validated in medical education contexts. Medical education leaders are encouraged to serially use this questionnaire to measure progress on their school's CQI focus.
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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 | 0.015 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".