Conceptual Guidelines for Developing and Maintaining Curriculum and Examination Objectives
Bibliographic record
Abstract
In an era of increasing professional accountability, there is a need for both medical educators and licensing bodies to identify attributes expected of medical graduates. Once these attributes are identified, educators must translate them into meaningful learning objectives. Because educators in many countries are in the process of defining (or have defined) attributes and competencies expected of their graduates, a review of the conceptual basis for writing curricular and examination objectives is relevant and constructive. The authors compare the principles of a conceptual model for identifying educational objectives and those used in the creation of the second (and most current) edition of the Objectives for the Qualifying Examination of the Medical Council of Canada (MCC). In developing these objectives, extensive and careful collaboration between licensing bodies, medical schools, the practicing profession, learners, and the MCC was critical. The process illustrates that the goals for the education of medical students can be consistent whether they are elaborated by medical schools or licensing bodies. The authors present the method and principles used by the MCC, including the clinical presentation model. The basic steps in the process are described: identifying the attributes, identifying the basic educational philosophy, assigning priority to problem-solving principles, and deducing learning objectives from desirable practice-related behaviors. The authors conclude with a consideration of the need and feasibility of defining the scientific underpinnings of competency-based learning objectives.
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 | 0.067 | 0.113 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".