A qualitative study examining stressors among Respiratory Therapists in Ontario amidst the COVID-19 pandemic
Bibliographic record
Abstract
Health care systems were subjected to an unprecedented surge of critically ill patients with the coronavirus disease 2019 (COVID-19), which required management by Respiratory Therapists (RTs). Despite the high level of burnout reported in this health care professional group, we have limited knowledge about the lived experience of RTs during the pandemic. This study aims to examine the impact of COVID-19 on RTs in Ontario, Canada. We conducted a qualitative exploratory, descriptive study by conducting virtual semi-structured interviews and focus groups with RTs between March 2023 and June 2023. Two coders analyzed the data using thematic analysis. Twenty-seven RTs participated in the study, with the majority being female (n = 25), averaging 16.4 years of practice (range 4 to 36 years), primarily in acute care settings (n = 23). We identified four themes and lessons learned from the perspective of RTs: (1) Working in the shadow and suffering in silence reflecting varying perceptions of recognition; (2) Flying blind amidst the buzz reflecting the rapid pace of changing policies and practices as COVID-19 gained global attention; (3) Putting out fires in the face of overflowing hospitals reflecting increased workload and staffing issues; and (4) Managing tensions, both external and internal reflecting how RTs coped with distressing workplace situations and their mental well-being. Finally, lessons learned from the RTs include 1) Mobilizing early and consistently during an emergency, which addresses staff concerns; 2) Prioritizing and investing in the mental health and well-being of RTs; 3) Implementing strategies to retain experienced staff in healthcare; and 4) Involving RTs in leadership discussions. The COVID-19 stressors of RTs have illuminated the detrimental impact of the pandemic on this understudied health care profession. With this knowledge, targeted interventions can be developed to address RT recognition and staff retention and provide mental health support.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.024 | 0.013 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".