Burnout of Support Personnel in the Cardiac Catheterization Laboratory
Bibliographic record
Abstract
Background: Healthcare professionals experience stressors in the workplace, putting them at elevated risk for burnout. The cardiac catheterization lab is a dynamic environment with high-acuity patients; however, little has been published investigating burnout syndrome among healthcare workers. The aim of the study was to identify the prevalence, demographic, and workload factors, which contribute to burnout syndrome among this population. Methods: This is a multicenter cross-sectional study assessing burnout with the Maslach Burnout Inventory (MBI) among registered nurses and registered cardiac invasive specialists working in the catheterization/electrophysiology lab and cardiac observation unit at four hospital centers in the metro Detroit area. Results: Of the 48 participants, 69% (n = 33) were female. The overall prevalence of burnout syndrome was 33% (n = 16). Significantly more males experienced burnout than females (P < 0.05). Of the participants experiencing burnout, a greater proportion worked in the catheterization lab compared to the cardiac observation unit (93.8% vs. 6.3%). Burned-out participants worked on average more day shifts, ST-segment elevation myocardial infarction (STEMI) call shifts, and extended day shifts per month compared to those not experiencing burnout. The rate of burnout was significantly higher for individuals reporting increased stress during the pandemic (69% vs. 18%, P < 0.05). Conclusions: Registered nurses and registered cardiac invasive specialists working in the cardiac catheterization or electrophysiology lab experience elevated levels of burnout. Greater attention should be placed in identifying and optimizing workplace variables which contribute to burnout among this population.
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 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 | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".