A survey of Canadian respiratory therapists working in the COVID-19 pandemic: The RRT perspective
Bibliographic record
Abstract
Introduction: Registered respiratory therapists (RRTs) are heavily involved in the care of individuals infected with COVID-19. The experiences and challenges specific to the RRT profession during the pandemic have not been qualified and the aim of this study is to bridge that gap. Methods: With institutional ethics approval, a cross-sectional survey was created through the survey software Redcap and made available online from 29 May to 6 July 2020. Any RRT working in Canada during the COVID-19 pandemic was eligible to participate. Responses to yes/no questions were calculated as frequencies and percentages, and free-text responses were summarized. Results: In total, 345 RRTs working in 11/13 of the provinces and territories, with varying years of experience completed the survey. The results reflected impacts of the pandemic that affected RRTs in a variety of ways at work, from being reassigned (30.7%) to caring for COVID-19 positive patients (57.4%) and intubated COVID-19 positive patients (50.7%). RRTs experienced communication issues around guidelines (66.7%) and some departments had run out of personal protective equipment (PPE; 19%). RRTs were personally impacted, including feeling overwhelmed by new and frequently changing guidelines (89.6%) and feeling concerned for themselves or their family members becoming infected because of their proximity to COVID-19 positive patients (89%). Discussion: RRTs reported being required to work more during the pandemic. The unpredictability and constantly changing schedules were sources of stress and anxiety. RRTs were faced with issues of overwhelming amounts of new information and had difficulty in disseminating it in a timely manner. Lack of guidance and lack of confidence in the current protocols added to the confusion, anxiety, and stress. RRTs were at the center of many high-risk moments for contracting the virus (intubation, extubation), and PPE shortages were a major challenge reported. RRTs working during the pandemic have been concerned for their own health and the health of their family members. Conclusion: The COVID-19 pandemic adds another layer of stress for RRT professionals who are working in high-risk situations and feel anxious, overwhelmed, and concerned about their personal safety.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".