Unintended consequences of COVID-19: Opportunities for respiratory therapists’ involvement in developing respiratory-related technologies
Bibliographic record
Abstract
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.
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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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.037 | 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".