Bibliographic record
Abstract
Competency-based medical education promises to provide effective and structured training, relying on the identification and measurement of trainee competency through standardized guidelines. Shifting to competency-based education approaches has provided the opportunity for training programs to re-examine and formally define core competencies representative of their scope of practice. Members of our team were involved in identifying the core surgical competencies that graduating residents of one specialty (Otolaryngology—Head and Neck Surgery (OTL-HNS)) needed to acquire. We used a modified Delphi approach wherein key stakeholders, including past and present program directors for one surgical subspecialty across Canada, were asked to rate all surgical procedures included in key specialty-specific policy documents and in a compiled comprehensive list of all procedures pertaining to OTL-HNS. We set out to engage in a data-driven approach to build consensus regarding core competencies for OTL-HNS. After several Delphi rounds, the polarization of participants became ingrained, and the act of selecting core competencies had the effect of both defining and failing to define the core aspects of the speciality. We found core competencies can, and do, overlap between specialties, representing a blurring of necessary competencies across specialties. This blurring could create overlapping or confounding professional identities and influence the accreditation of residency programs. This paper will not report on the findings derived from the Delphi process, but rather describes insights gained throughout our failed consensus process and explore the unintended consequences of attempting to define core competencies in one surgical specialty and how it ultimately led to the termination of our research and consensus-building initiative.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.794 | 0.574 |
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".