Bibliographic record
Abstract
Just over a year ago, industries from around the world began to feel the effects of a financial crisis that is now spreading across most areas of the business community, including hospitals and device manufacturers. The knee-jerk reaction is often to cut spending and control costs in every sector.Adding to the difficulty for hospitals, which now must become more efficient and—as a result—cut budgets while maintaining service levels, government funding for health programs is also affected. In the United States and Canada, manufacturers are being forced to reduce medical benefits, and growing unemployment is increasing the number of those who are uninsured, contributing to the decline in elective healthcare, which also affects hospital revenues. And although the federal stimulus package approved by President Obama in February provides support in the way of Medicaid for the unemployed and subsidies for manufacturers' benefits programs, which would help hospitals, current pressures will still exist for some time. Many of you are already experiencing slashed or frozen budgets.So what can we do to cope with the economic downturn? In this issue of BI&T, the Reader Forum column highlights efforts to control costs, for example, by maintaining minimum inventory levels, staggering work hours, and limiting costs of contracts. Also, in the BMET Resource File column in this issue, Morgan Hall—a biomed from California—talks about increasing his department's value by overseeing management of the hospital system's copiers. The move has saved his facility more than $100,000 a year.Another excellent resource for understanding and coping with the current economic situation is the AAMI economy webpage, Living With the New Economy, at www.aami.org/economy.Whatever strategies you employ, communicate with your staff when things are happening at your facility. Quash the rumors, and help staff stay focused on their jobs and open to new ideas that may—sometime soon—help solve the problem.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.022 | 0.014 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.049 | 0.015 |
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".