Role of Medical Students in Responding To COVID-19: Identifying and Addressing Vital Deficiencies
Bibliographic record
Abstract
To address the shortage of personal protective equipment (PPE) that occurred as a result of Coronavirus disease 2019 (COVID-19), three first-year medical students at Western University developed an initiative to deliver handmade gowns to primary care providers in London, Ontario, Canada. They partnered with the local branch of the Canada Sews organization to sew the gowns, and created a gown order form which was distributed in the community. Following gown delivery by the authors, an optional feedback form was sent to the gown recipients for quality assurance purposes. As of June 10, 2020, 411 gowns were delivered to medical and dental locations, long-term care homes, emergency shelters, and pharmacies. Feedback from the recipients indicated that the gowns were comfortable to wear and consistently useful to primary care practices. The successful execution of the initiative within a month of its inception, the delivery of more than 400 gowns within the subsequent month, and the positive feedback from the gown recipients, indicates that medical students can play an important role during the COVID-19 pandemic, and other periods of crisis, even outside of clinical settings. Specifically, they are able to demonstrate the qualities of leadership, collaboration, and advocacy to spearhead initiatives to fulfill unmet community needs. They are also uniquely situated to help their communities due to factors such as the skills and knowledge they have attained in their academic training. Thus, the ability of medical students to assist primary care providers should be taken into consideration for future pandemics.
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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 | 0.018 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".