A comprehensive process of content validation of curriculum consensus guidelines for a medical specialty
Bibliographic record
Abstract
In this article, we outline an innovative and comprehensive approach to the development by consensus of curriculum content guidelines for a medical specialty. We initially delineated the content domain by triangulation of sources, validated a curriculum blueprint by both quantitative and qualitative methodology, and finally reached consensus on content by Delphi methodology. Development of curricular objectives is an important step in curriculum development. Content definition or "blueprinting" refers to the systematic definition of content from a specified domain for the purpose of creating test items with validity evidence. Content definition can be achieved in a number of ways and we demonstrate how the concepts of content definition or validation can be transferred beyond assessment, to other steps in curriculum development and instructional design. Validity in Education refers to the multiple sources of evidence to support the use or interpretation of different aspects of a curriculum. In this approach, there are multiple sources of content-related validity evidence which, when accumulated, give credibility and strength to curriculum consensus guidelines.
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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 | 0.480 | 0.586 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.021 | 0.009 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.007 | 0.016 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".