Academic coaching of medical students during the COVID‐19 pandemic
Bibliographic record
Abstract
a finance department partnership to create a portal for tax-deductible donations, and access to information technology licenses for volunteer management and communication.These institutional links facilitated our support of systems-level needs and granted administrators a streamlined connection to a previously decentralised volunteer network.Although many student organisations operate independently of their universities, members of these grassroots initiatives can often be identified by their common academic institution and may inadvertently create legal vulnerabilities for themselves and their institutions.Integrating UNMC CoRe into the ICS chain of command provided greater legal protection to our volunteers.For example, student leaders gained insight into critical language for volunteer release forms by working with university risk management services.This coordination also ensured that volunteers employed proper precautions when providing child care for health care workers.Finally, the ICS framework facilitated multidisciplinary collaboration.Academic health centres often consist of multiple independent professional schools, which contributes to siloed volunteer structures designed by and for specific health professions.Consolidating within the ICS framework helped our organisation to galvanise a campus-wide, interprofessional effort with a diverse volunteer pool.Our fundamental reflection is that the formal pairing of learner-led initiatives with institutional resources fosters innovation from students and academic health centre leaders alike.In the coming months, we intend to formally assess qualitative outcomes derived by student volunteers.The integration of UNMC CoRe into the UNMC ICS structure sets an important precedent for the formal consideration of student-led initiatives within institutional emergency preparedness and response efforts.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".