Unused Medication Collection: An Emerging Service-Learning Experience for Pharmacy Students
Bibliographic record
Abstract
While the U.S. makes up around 5% of the world’s population, we consume approximately 75% of the world’s prescription drugs—well over 4 billion prescriptions per year. Approximately one third of those are never used, creating an array of public health challenges. These challenges include land and water pollution; unintentional inappropriate use and unintentional human, pet, and wildlife poisonings; and intentional drug abuse and diversion. Misuse of prescription drugs now exceeds that of all illegal drugs combined. Reducing the number of medications prescribed is of primary importance, as well as collecting unnecessary medications from households and disposing of them through environmentally friendly methods. There are a variety of medication collection methods, including public take-back events, permanent drop boxes at pharmacies, collection by police departments, as well as prepaid mailers available from some pharmacies and the Internet. When bulk medication is collected at take-back events or by police departments, it is generally quantified as “pounds collected,” with no determination of specific medications collected, quantities of prescribed medications left unused, or length of household storage beyond the expiration date. Pharmacy students are uniquely positioned to explore these unknowns and develop solutions with their high level of drug expertise. This research study combined with community service events involved the collaboration of pharmacy students with environmentalists, community volunteers, and law enforcement officers to collect and analyze unwanted medications, as well as explore factors pertinent to this public hazard.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".