Burnout, career satisfaction, and well-being among US neurologists in 2016
Bibliographic record
Abstract
OBJECTIVE: To study prevalence of and factors that contribute to burnout, career satisfaction, and well-being in US neurologists. METHODS: A total of 4,127 US American Academy of Neurology member neurologists who had finished training were surveyed using validated measures of burnout, career satisfaction, and well-being from January 19 to March 21, 2016. RESULTS: Response rate was 40.5% (1,671 of 4,127). Average age of participants was 51 years, with 65.3% male and nearly equal representation across US geographic regions. Approximately 60% of respondents had at least one symptom of burnout. Hours worked/week, nights on call/week, number of outpatients seen/week, and amount of clerical work were associated with greater burnout risk. Effective support staff, job autonomy, meaningful work, age, and subspecializing in epilepsy were associated with lower risk. Academic practice (AP) neurologists had a lower burnout rate and higher rates of career satisfaction and quality of life than clinical practice (CP) neurologists. Some factors contributing to burnout were shared between AP and CP, but some risks were unique to practice setting. Factors independently associated with profession satisfaction included meaningfulness of work, job autonomy, effectiveness of support staff, age, practicing sleep medicine (inverse relationship), and percent time in clinical practice (inverse relationship). Burnout was strongly associated with decreased career satisfaction. CONCLUSIONS: Burnout is common in all neurology practice settings and subspecialties. The largest driver of career satisfaction is the meaning neurologists find in their work. The results from this survey will inform approaches needed to reduce burnout and promote career satisfaction and well-being in US neurologists.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".