Exploring the Realities of Curriculum-by-Random-Opportunity: The Case of Geriatrics on the Internal Medicine Clerkship Rotation
Bibliographic record
Abstract
BACKGROUND: While major clerkship blocks may have objectives related to specialized areas such as geriatrics, gay and lesbian bisexual transgender health, and palliative care, there is concern that teaching activities may not attend sufficiently to these objectives. Rather, these objectives are assumed to be met "by random opportunity".((1)) This study explored the case of geriatric learning opportunities on internal medicine clinical teaching units, to better understand the affordances and limitations of curriculum by random opportunity. METHODS: Using audio-recordings of morning case review discussions of 13 patients > 65 years old and the Canadian geriatric core competencies for medical students, we conducted a content analysis of each case for potential geriatric and non-geriatric learning opportunities. These learning opportunities were compared with attendings' case review teaching discussions. The 13 cases contained 40 geriatric-related and 110 non-geriatric-related issues. While many of the geriatric issues (e.g., delirium, falls) were directly relevant to the presenting illness, attendings' teaching discussions focused almost exclusively on non-geriatric medical issues, such as management of diabetes and anemia, many of which were less directly relevant to the reason for presenting to hospital. RESULTS: The authors found that the general medicine rotation provides opportunities to acquire geriatric competencies. However, the rare uptake of opportunities in this study suggests that, in curriculum-by-random-opportunity, presence of an opportunity does not justify the assumption that learning objectives will be met. CONCLUSIONS: More studies are required to investigate whether these findings are transferrable to other vulnerable populations about which undergraduate students are expected to learn through curriculum by random opportunity.
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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 | 0.017 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".