Using Group Concept Mapping to Explore Medical Education’s Blind Spots
Bibliographic record
Abstract
PHENOMENON: All individuals and groups have blind spots that can lead to mistakes, perpetuate biases, and limit innovations. The goal of this study was to better understand how blind spots manifest in medical education by seeking them out in the U.S. APPROACH: We conducted group concept mapping (GCM), a research method that involves brainstorming ideas, sorting them according to conceptual similarity, generating a point map that represents consensus among sorters, and interpreting the cluster maps to arrive at a final concept map. Participants in this study were stakeholders from the U.S. medical education system (i.e., learners, educators, administrators, regulators, researchers, and commercial resource producers) and those from the broader U.S. health system (i.e., patients, nurses, public health professionals, and health system administrators). All participants brainstormed ideas to the focus prompt: "To educate physicians who can meet the health needs of patients in the U.S. health system, medical education should become less blind to (or pay more attention to) …" Responses to this prompt were reviewed and synthesized by our study team to prepare them for sorting, which was done by a subset of participants from the medical education system. GCM software combined sorting solutions using a multidimensional scaling analysis to produce a point map and performed cluster analyses to generate cluster solution options. Our study team reviewed and interpreted all cluster solutions from five to 25 clusters to decide upon the final concept map. FINDINGS: Twenty-seven stakeholders shared 298 blind spots during brainstorming. To decrease redundancy, we reduced these to 208 in preparation for sorting. Ten stakeholders independently sorted the blind spots, and the final concept map included 9 domains and 72 subdomains of blind spots that related to (1) admissions processes; (2) teaching practices; (3) assessment and curricular designs; (4) inequities in education and health; (5) professional growth and identity formation; (6) patient perspectives; (7) teamwork and leadership; (8) health systems care models and financial practices; and (9) government and business policies. INSIGHTS: Soliciting perspectives from diverse stakeholders to identify blind spots in medical education uncovered a wide array of issues that deserve more attention. The concept map may also be used to help prioritize resources and direct interventions that can stimulate change and bring medical education into better alignment with the health needs of patients and communities.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".