Maximizing co-training opportunities on a traditional health sciences campus
Bibliographic record
Abstract
Both the economics and the science of modern healthcare demand that the best patient care be delivered by an integrated team of healthcare providers, each expert in their own field, but also expert in the ability to function well as a team member. Functioning as a member of a complex team is not intuitive, and even the best educated among us needs additional instruction to do this well. But even the best schools of nursing and medicine, especially those with longer histories and more traditional curricula, may not be designed to support this type of instruction. Practical considerations such as accreditation needs, administration, budget lines, and even physical facilities tend to “silo” instruction by discipline. We argue that even in institutions with traditional curricula, there are numerous opportunities to co-train nursing, medical, and other healthcare students and faculty if we remain open to possibilities. This article presents five brief case-studies of co-training events where nursing, medical, and other healthcare students and/or faculty learn in the same environment with minimal administrative effort including: (1) the Certificate in Health Professions Education program; (2) workshops on Increasing Cultural Competence; (3) the iCOPE project in interdiscip- linary palliative care; (4) joint daily rounding in an urban children’s hospital; and (5) providing care in the Teen Age Parent Program (TAPP).
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".