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Record W3048227458 · doi:10.12927/hcq.2020.26282

In a Time of Need: A Grassroots Initiative in Response to PPE Shortage in the COVID-19 Pandemic

2020· article· en· W3048227458 on OpenAlexaffvenueabout
Meera Shah, Jordan Ho, Adrina Zhong, Matthew Fung, Mario Elia, Janet Dang, Thomas R. Freeman

Bibliographic record

VenueHealthcare Quarterly · 2020
Typearticle
Languageen
FieldMedicine
TopicInfection Control and Ventilation
Canadian institutionsMiddlesex London Health Unit
Fundersnot available
KeywordsPersonal protective equipmentGrassrootsPandemicMedicineHealth careEconomic shortageCoronavirus disease 2019 (COVID-19)Scope of practiceMedical emergencyNursingFamily medicineInfectious disease (medical specialty)Political scienceDisease

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score0.328

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.066
GPT teacher head0.368
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2020
Admission routes3
Has abstractyes

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