The initial impact of <scp>COVID</scp>‐19 on Australasian Sonographers Part 1: Changes in scan numbers and sonographer work hours
Bibliographic record
Abstract
Introduction: COVID-19 has seen a series of lockdowns and suspension on non-urgent elective surgeries. Subsequently, there was a drop in the number of diagnostic imaging services billed in April, May, 2020. A survey was undertaken from March to June 2020 to determine the initial impact of COVID-19 on Australasian Sonographers. This article, the first in a 3-part series presents and discusses the results of this survey pertaining to changes in the number of scans performed, and changes in the working hours of sonographers. The remaining two articles in this series address other initial COVID-19 impacts on Australasian Sonographers. Methods: An online survey was conducted containing questions regarding changes to work hours and examination numbers. Results: 444 participants answered the survey. Seventy eight percent of sonographers reported a decrease in the number of examinations being performed in their department A decrease in work hours was reported by 68% of sonographers with almost a quarter of these reporting that they had lost all their hours. A higher percentage of work hours changes were seenin private practices. Many reductions in work hours were reported to be voluntary. Conclusion: Scan numbers in ultrasound departments were affected by COVID-19, as were sonographers' work hours.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".