Bibliographic record
Abstract
To the Editor: Drs. Hoang and Lau1 recently offered a discerning perspective on what they view as reductionist problems in competency-based medical education (CBME) using overfitting in data science as an illustrative parallel. The authors went on to propose several ways in which qualitative methods may complement the tradition of quantitative measurement in student assessment. While they justly acknowledge the necessity of multiple methods of assessment in CBME, and its associated burden, here I believe the authors have foregone the opportunity to further explore cross-discipline contributions. Presumably, membership of the described “faculty panels” for periodic group assessment of medical trainees is limited to physicians, and while the exemplar coaching program employs trained nonclinicians, the authors inadequately address how other professionals, including nurses and pharmacists, might support feedback for student learning. The processes (and benefits) of structured, multisource feedback for physicians are well described, and the study of its use in undergraduate and postgraduate health professional student education is beginning to gain wider attention.2 Clinicians in multidiscipline training settings work in close proximity with students—directly observing tasks, cooperating in patient care—and are acquainted with competency roles that overlap with their own discipline or are broadly interprofessional in nature (such as professionalism, communication, and collaboration).3 Health professions education now additionally emphasizes the “team readiness” of its students, so why not capitalize on the viewpoints of the team members themselves? Research demonstrates that physician supervisors are certainly far from uniform as raters when making performance judgments, and so a relative “multiplicity of perspectives” is already inherent in medical trainee assessment. However, upscaling assessor capacity in CBME will benefit from more concerted attention to understanding and formalizing feedback from the diversity of professionals interacting with trainees in learning contexts over time.4 Kerry Wilbur, PharmD, MScPHAssociate professor and executive director, Entryto-Practice Education, Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, British Columbia, Canada; [email protected]; ORCID: http://orcid.org/0000-0002-5936-4429.
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 | 0.043 | 0.253 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.015 | 0.035 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".