Towards high reliability in national pathology education: Evaluating the United States and Canadian Academy of Pathology educational product
Bibliographic record
Abstract
The United States and Canadian Academy of Pathology (USCAP) leadership undertook a high level, global review of educational product outcomes data using high reliability organization (HRO) principles: preoccupation with failure; reluctance to simplify; sensitivity to operations; commitment to resilience; and deference to expertise. HRO principles have long been applied to fields such as aviation, nuclear power, and more recently to healthcare, yet they are rarely applied to the field that underpins these-and many other-complex systems: education. While errors in education are less calamitous than in air travel or healthcare delivery, USCAP's educational products impact over 15,000 learners a year, and thus have important implications for the future practice of pathology. Here we report USCAP's experiences using HRO principles to evaluate our keystone educational product, the "USCAP Short Course." Following this novel method of data review, USCAP leadership was able to better understand diverse learner needs based on practice venue, training level, and course topic. Unexpected lessons included the identification of specifically challenging educational topics, such as molecular pathology, and a need to focus more resources on emerging fields such as quality and patient safety. The results allow USCAP to assess educational product performance using HRO tools, and provide strong data-driven decision support for future national pathology education strategy.
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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 | 0.175 | 0.315 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".