Using a podcast to increase awareness of burnout in nurse anesthesia providers
Bibliographic record
Abstract
The incidence of burnout among healthcare providers is rising. Certified Registered Nurse Anesthetists (CRNAs) and Student Registered Nurse Anesthetists (SRNAs) are at a particularly high risk due to the inherent stress of the role. This multifaceted issue poses significant concerns to CRNA and SRNA wellbeing, patient safety, and healthcare organizations. The purpose of this project was to evaluate and compare burnout levels among CRNAs and SRNAs working at a large academic medical center and to increase awareness of the consequences of burnout through an educational podcast. Additionally, the development and dissemination of a two-part educational podcast aims to increase awareness of the consequences of burnout and introduce listeners to evidence-based resources to mitigate it. The data collection tool utilized was the Maslach Burnout Inventory- Human Services Survey for Medical Personnel (MBI-HSS). Literature demonstrates this 22-item questionnaire is a valid and reliable tool. A podcast was created to disseminate survey results as well as discuss the phenomenon of burn out. Lastly, the episodes were published to Spotify and Apple Podcasts. This project seeks to provide foundational knowledge of the incidence of burnout among CRNAs and the SRNA community in order to identify needs for future quality improvement interventions for these populations. Increasing awareness of one’s own level of burnout is a necessary first step to begin to address burnout in this community.
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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".