Don’t Waste the Medical Waste: Reducing Improperly Classified Hazardous Waste in a Medical Facility
Bibliographic record
Abstract
Hospitals in the United States generate over one million tons of waste each year, approximately a quarter of which is classified as hazardous medical waste. There are environmental, infectious, and financial burdens associated with this waste, and those burdens increase significantly when the waste is hazardous. Managing waste can be difficult, but training is an effective approach to reducing waste. We conducted a quality improvement project at the Johns Hopkins Hospital Pathology Core Laboratory from 2015 through 2017. We hypothesized that improved staff training is an effective way to reduce the amount of general waste in a medical facility that is incorrectly classified as hazardous. Two interventions were identified and implemented, the first being a series of classroom-based training sessions and the second being a simple informational poster that was displayed over waste bins. The impact of the training and posters was measured by two surveys that were performed before and after the interventions. The first survey was a web-based instrument that was completed by laboratory staff, while the second survey was observational and measured how many bins contained improperly disposed waste. Some of the data from these interventions supports the hypothesis. The observational survey, in particular, recorded an increase in proper waste disposal from 7.5% to 71.9% following the classroom intervention. Future studies can help determine if these improvements can be increased and sustained over time by assessing the cost and benefit of different interventions and measuring how long the gains from each can be sustained.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".