Practitioner-Identified Priorities for Dedicating Resources to Address Workplace Factors Impacting Sonographer Health and Well-Being
Bibliographic record
Abstract
Objectives: Supporting sonographers' health and well-being requires targeting the workplace factors they identify as most influential. This study examined sonographers' perspectives to guide priorities for resource allocation and solution development. Materials & Methods: An online survey of 1,276 U.S. and Canadian sonographers asked participants to rate 30 workplace factors across four categories-workflow, equipment/physical environment, administrative/organizational environment, and training/health practices-on their perception of impact on well-being and importance for understanding and improving. Respondents selected up to five factors as the highest priorities for immediate action and resource allocation. Results: Five factors ranked among the top ten across specialties and were rated as significantly impactful and important by more than two-thirds of respondents: productivity requirements (76.9% impact, 74.3% importance), staff scheduling policies (73.2%, 74.4%), supervisor support (72.1%, 71.2%), exam room furniture (73.1%, 68.0%), and ultrasound machine design (69.3%, 75.6%). Exam scheduling was the top priority across all practice areas and the only factor identified in the top five by a majority of all respondents. Conclusions: While specialty area differences exist, sonographers consistently identified administrative and organizational factors as the most impactful, important, and of highest priority for immediate action. Addressing these concerns requires contextualized solutions developed with direct practitioner input.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".