Bibliographic record
Abstract
BACKGROUND: Curriculum development in the health sciences usually entails a lengthy, in-depth review of most or all aspects of the curriculum. The review usually leads to the generation of a detailed report that is submitted to the Dean or executive committee of the faculty. Much has been written about the process of curriculum development but very little has been written about the important processes of curriculum renewal and revision. AIMS: Health sciences curricula, including those that are newly developed, will benefit from timely periodic revision. The revision process with subsequent diligent curriculum monitoring is called curriculum renewal. In this article, we articulate twelve tips on how to assure dynamic, ongoing curriculum renewal. The overall goal of the renewal should be to assure timely, evidence-based curriculum responsiveness to changes in practice, health care, student needs and educational approaches based on quality research. METHODS: We searched the health care education literature for articles related to curriculum development, seeking credible evidence on, and recommendations for, best practices for ongoing renewal of developed curricula. RESULTS AND CONCLUSIONS: The health sciences literature is replete with recommendations to guide suggestions for curriculum development; however, there are few credible research-based guidelines to inform dynamic curriculum renewal. Given the rapid development of research-based knowledge in health sciences education practices, there is a need to diligently monitor the ongoing successes and failures of a developed curriculum with a view to instituting large or small timely changes to assure timely curriculum renewal.
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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 | 0.066 | 0.209 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".