Abstract 282: Understanding Current Organizational Strategies to Support Physician Well‐Being in Stroke, Neurocritical Care, and Neurointerventional Practice
Bibliographic record
Abstract
Introduction Burnout in medicine is an occupational hazard and has emerged as a pressing concern in recent years. Organizational changes can be impactful in countering burnout (1). The factors leading to burnout in medicine are multifaceted, encompassing organizational factors such as workload, inadequate support, and inefficient administrative systems. The purpose of this study is to investigate current organizational measures to support physician well‐being. This study was an initiative led by the SVIN wellness committee. Methods A 39‐question online survey investigating current organizational well‐being practices was distributed to physicians both nationally and internationally practicing stroke, neurocritical care, and interventional neurology. Data analysis was performed using Python, utilizing the libraries “pandas” and “sklearn”. Results This study analyzed burnout among 109 healthcare professionals, predominantly from the U.S. (93.6%) and Canada (6.4%). The majority were neurointerventional specialists (53.2%), aged 35‐44 years (52.3%), and male (62.4%). Burnout frequency was measured on a 0 (Never) to 4 (Every day) scale. Using a Random Forest model, the study identified key burnout predictors from questions, which covered organizational leadership, wellness resources, compensation, and workload. The most influential predictors were: adequacy of compensation relative to specialty, workload, and stress (17.7% importance); leadership accountability for workforce wellbeing (9.1%); and establishment of wellness or burnout as a critical metric (7.5%). Other factors included provision of wellness screenings (7.1%), and dedication of resources toward professional well‐being (4.9%). Conclusion The study identifies compensation, leadership accountability, and wellness resources as key predictors of burnout among healthcare professionals. These findings highlight the need for targeted organizational strategies to enhance physician well‐being and mitigate burnout. Further research is warranted to validate and expand upon these findings.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".