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Record W3084622072 · doi:10.29390/cjrt-2020-032

Unintended consequences of COVID-19: Opportunities for respiratory therapists’ involvement in developing respiratory-related technologies

2020· article· en· W3084622072 on OpenAlexaffvenueabout
Patricia McClurg, Nikolay Moroz, Marco Zaccagnini

Bibliographic record

VenueCanadian Journal of Respiratory Therapy · 2020
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsMcGill University Health CentreMcGill University
Fundersnot available
KeywordsPandemicPersonal protective equipmentCoronavirus disease 2019 (COVID-19)Health careRepurposingBusinessGovernment (linguistics)Public relationsWorkforceUnintended consequencesFace shieldMedicinePolitical scienceEngineeringLaw

Abstract

fetched live from OpenAlex

More than ever, the role of the registered respiratory therapist (RRT) is critical to Canadians’ health. Nationally, RRTs are mobilizing their efforts to battle a novel viral enemy. Members of the Canadian RRT community, from clinicians to educators to students, are meeting the challenge of the COVID-19 pandemic crisis. RRTs have consistently demonstrated innovation, professionalism, and a commitment to providing evidence-based care for patients through the profession’s 50-year history; this remains unchanged and arguably more apparent considering the COVID-19 pandemic. RRTs’ clinical experience, expertise, and academic training render RRTs indispensable in some unintended consequences of the COVID-19 pandemic. One of these unintended consequences of the COVID-19 pandemic is the rapid development of innovative respiratory-related technologies. The need to develop new respiratory technology A significant consequence of the COVID-19 pandemic is the shortage of medical equipment (e.g., personal protection equipment [1]). On 20 March 2020, the Government of Canada asked Canadian businesses and industrialists to help develop and manufacture supplementary health care supplies for health professionals [2]. Since the call, almost 3,000 companies have volunteered their engineering and manufacturing expertise, including their facilities, to produce medical equipment. Some include clothing brands repurposing stock to provide medical gowns and sports manufacturers providing face shields [3, 4]. Comparisons of this type of industry collaboration have been made to automobile companies’ wartime efforts—including Ford and GM—to produce tanks and airplanes using their factories during World War II [5]. RRTs working directly with patients with COVID-19 have the first-hand experience and knowledge to be invaluable counsel for these companies on medical equipment requirements. Another significant consequence of the pandemic includes a potential shortage of available critical care mechanical ventilators [6]. The Canadian government acknowledged the potential shortage and focused its efforts to secure and manufacture Canadian-made mechanical ventilators. Additionally, the Canadian-made mechanical ventilators would be distributed to other countries if not required in Canada [7]. The call for Canadian-made mechanical ventilators mobilized many medical experts and entrepreneurs to design and produce easy-to-use mechanical ventilators rapidly. Collaboration across the world has already resulted in many ventilator prototypes and adjunct therapy devices. Some were designed by large established medical companies [8], while some made by small, independent teams [9]. These devices need to be built quickly and inexpensively with readily available hardware and infrastructure. The devices must be user-friendly so that all health care professionals are easily able to learn to use them while still ensuring minimal safety standards.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.880
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.356
GPT teacher head0.395
Teacher spread0.039 · 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.

Study designNot applicable
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

Citations3
Published2020
Admission routes3
Has abstractyes

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