Work Systems Factors Associated With Burnout in Sonographers Working in the United States and Canada
Bibliographic record
Abstract
Objective:Burnout in sonographers is a prevalent and complex professional hazard associated with poorer quality of work life, productivity, and patient outcomes. This study aimed to understand the prevalence of and work systems factors associated with burnout among a large sample of sonographers.Materials and Methods:Research study registry participants (n = 3659) were invited to complete a follow-up survey about personal and work environment factors, including work hours, break time, coworker and supervisor support, and job satisfaction, overall health, and sleep quality. The Copenhagen Burnout Inventory assessed personal, work-related, and client burnout. Simple and multiple linear regressions were used to identify work systems factors associated with burnout.Results:Of 1389 respondents, over half reported moderate-to-severe personal and work-related burnout, while one-quarter reported moderate-to-severe client burnout. Higher work-related burnout was associated with younger age, working in the Western United States, working full-time, taking fewer weekly break hours, poorer overall health and sleep quality, and lower supervisor support and job satisfaction. Client burnout was associated with poorer sleep quality and lower job satisfaction.Conclusion:Burnout was prevalent in a large sample of sonographers. Multilevel work systems factors are associated with burnout, suggesting collaboration among sonographers, administrators, and organizations is needed to address burnout.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".