In a Time of Need: A Grassroots Initiative in Response to PPE Shortage in the COVID-19 Pandemic
Bibliographic record
Abstract
SETTING: Primary care is the first line of defence in healthcare, particularly during the coronavirus disease 2019 (COVID-19) pandemic. In the London-Middlesex region of Ontario, a critical shortage of personal protective equipment (PPE) was identified among primary care physicians (PCPs). INTERVENTION: With the help of the London-Middlesex Primary Care Alliance, volunteer administrators, physicians and medical students coordinated the acquisition and redistribution of community-donated PPE to PCPs across London-Middlesex. Our scope evolved to include PPE reusability and stewardship and PCP wellness. OUTCOME: Beginning on March 16, 2020, our initial four-week operation provided PPE to over 200 PCPs. We received 60 donations, including over 118,000 gloves, 13,700 masks, 700 wellness kits and reusable cloth masks and gowns. Each delivery included educational pamphlets, and our online PPE stewardship session was attended by over 30 physicians. IMPLICATIONS: In response to the PPE shortage in COVID-19, our efforts evolved into a complex adaptive system, supported by an organizational body with a pre-existing communication infrastructure, to great success. Our scope extended beyond simple PPE provision to PCPs. Furthermore, our initiative established a framework for a centralized response to PPE shortage in Ontario Health West.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".