Challenges in shifting to an integrated curriculum in a Caribbean medical school
Bibliographic record
Abstract
Xavier University School of Medicine (XUSOM) is an offshore Caribbean medical school in Aruba, Kingdom of the Netherlands admitting students from the United States, Canada and other countries to the undergraduate medical (MD) course. Like most other offshore Caribbean medical schools, XUSOM was initially following a discipline based curriculum but shifted to an integrated curriculum from January 2013. Initially the school was following a partially integrated curriculum with the normal human subjects of anatomy, physiology and biochemistry being covered during the first two semesters and the abnormal human subjects of pathology, microbiology, pharmacology and introduction to clinical medicine being covered during semesters 3 and 4. From January 2014 the school has shifted to a fully integrated curriculum with all the basic science subjects being covered in an integrated organ system based manner [1]. Table 1 shows the different systems being learned by students during different semesters. At XUSOM like in most other offshore Caribbean medical schools a semester of study is of 15 weeks duration and there are three student intakes a year in January, May and September [2]. In this article I will briefly discuss challenges faced in shifting to a fully integrated curriculum and how we have tried to address them.
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 | 0.023 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".