ReThinking clinical reasoning: A paradigm shift
Bibliographic record
Abstract
Numerous studies have demonstrated that our healthcare systems and medical education programs are fundamentally flawed. In North America and Europe, most systems were built upon values and structures that have historically benefitted middle and upper class males of European descent in the global north. As a result, there continue to be systemic biases that are pervasive throughout our healthcare systems and medical education programs. This has led to inequities in health outcomes and clinical reasoning practices which marginalize several communities. These biases are perpetuated as we continue to lead medical education research and practice with traditional values and views of evidence. To address these issues, we proposed a 'flipped' conference in which three interdisciplinary writing teams, comprised of both junior and senior academics, clinicians, and researchers, were invited to rethink the foundations of clinical reasoning. In the months leading up to the conference, each writing team explored a specific topic related to clinical reasoning and racial equity. The papers, presented during the virtual conference are now available in this issue of the Journal for the Evaluation of Clinical Practice. In addition, 6 more publications were added to this special topic to showcase new evidence and theory that builds on the recommendations in the three core papers.
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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 | 0.330 | 0.209 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.013 | 0.108 |
| Scholarly communication | 0.035 | 0.048 |
| Open science | 0.010 | 0.026 |
| Research integrity | 0.017 | 0.042 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".