Burnout among respiratory therapists during COVID-19 pandemic
Bibliographic record
Abstract
Background Respiratory therapists (RTs) faced many unpredicted challenges and higher stress levels while managing critically ill patients with the coronavirus disease (COVID-19). This study's primary objective was to evaluate the compassion satisfaction and compassion fatigue among RTs in the United States during the COVID-19 pandemic. Methods This cross-sectional, descriptive, survey-based study conducted from July 2020 to August 2020 was administered to all active members of the American Association of Respiratory Care via AARConnect. RTs' characteristics including personal, job-specific, and organizational factors were collected. Professional Quality of Life Scale (ProQOL, version 5) was used to measure compassion satisfaction and fatigue. Results A total of 218 participants fully completed the survey, 143 (65.6%) were female, 107 (49.1%) were between 35 and 54 years of age and 72 (33%) were above 55 years of age. Compassion satisfaction was moderate in 123 (56.4%) and high in 93 (42.7%) RTs. Higher compassion satisfaction was found in RTs who have a higher salary (P = 0.003), work overtime (P = 0.01), hold leadership positions (P \< 0.001), work in research/education (P \< 0.001) and work for departments that provide help in managing burnout and stress (P = 0.007) and that promote a positive work environment (P \< 0.001). Burnout score was low in 90 (41.3%) and moderate in 127 (58.3%) RTs. Higher burnout was found among younger RTs (P = 0.019), those with fewer years of experience (P = 0.013) and those with less than a year at their current job (P = 0.045). Secondary traumatic stress (STS) was low in 106 (48.6%) and moderate in 112 (51.4%) RTs. Higher STS levels were noted among younger RTs (P = 0.02) and RTs with lower education levels (P = 0.016). Conclusion This survey study identified various personal, job and organizational related factors associated with increased compassion satisfaction as well as compassion fatigue among RTs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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 teacher head, 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".