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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".