Many Minds, One Model: Exploring Decision Making of an Undergraduate Medicine Competency Committee Using the Construct of a Shared Mental Model
Bibliographic record
Abstract
Introduction: Competency committees (CCs) are considered mandatory in competency-based medical education. There remains insufficient research to guide programs in optimizing the work of CCs especially in the undergraduate context. In order to address this gap, the functioning of an undergraduate CC is examined using the construct of a shared mental model (SMM) to explore factors and context that inform a holistic review of performance. Methods: A qualitative exploratory study was conducted. Using purposive sampling, 10 members of a Student Progress Committee (SPC) participated in 60-minute, semi-structured interviews (April 2022 to June 2023). An abductive thematic analysis approach generated themes which were then mapped onto a mental model construct. This heuristic helped construct and visualize the inner workings of a SMM as a holistic decision-making process that operates on manipulating multiple data inputs (quantitative and qualitative) in order to generate robust outcomes. Results: SPC members shared similar expectations of the task at hand while having multiple and conflicting perspectives about inputs important for decision making. Members grappled with what they perceived as a subjective process but agreed that having principles specific to holistic decision making can generate robust outcomes. Diversity of group membership was essential for minimizing member bias and group conformity in decision making. Discussion: This new understanding of how CCs operate at the undergraduate level can inform the SPC and guide its members in their quality improvement efforts and inform broader program-wide improvement, locally; moreover, it may contribute to the ongoing improvement of CCs in other settings.
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 | 0.024 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| 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".