To Help or Not to Help: A First Year Canadian Medical Student’s Dilemma During the COVID-19 Pandemic.
Bibliographic record
Abstract
I am a first-year medical student, and this is a commentary, highlighting some of the dilemmas and challenges encountered by a first-year medical student during these unprecedented times of the COVID-19 crisis. With the declaration of COVID-19 as a public health emergency, and medical students having to discontinue their clinical duties, I felt apprehensive. As if being restricted from serving the communities for whom I took an oath of service, even before I could start. Talking with my mentors and through self-reflection, I found solace in diverting my energy in supporting the frontline staff from the bleachers. This article would provide medical students with an opportunity to think critically during these times, stir conversation amongst medical students, and allow them to recognize how to reconcile with so much uncertainty about the future.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.042 | 0.017 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.026 | 0.051 |
| Insufficient payload (model declined to judge) | 0.005 | 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".