Caregiver Practices and Knowledge Regarding Leftover Prescription Medications in Homes With Children
Bibliographic record
Abstract
OBJECTIVES: The aims of the study were to determine the frequency of and factors associated with leftover or expired prescription medication ("leftover medication") presence in homes with children and to assess caregivers' reported behaviors and knowledge regarding disposal of leftover medications in the home. METHODS: This study is a planned secondary analysis from a survey of primary caregivers of children aged 1 to 17 years presenting to an emergency department. The survey assessed leftover medications in the home and medication disposal practices, knowledge, and guidance. The survey was developed iteratively and pilot tested. Multivariable logistic regression was used to identify factors associated with leftover medication presence in the home. RESULTS: We enrolled 550 primary caregivers; 97 of the 538 analyzed (18.0%; 95% confidence interval [CI], 14.8-21.5) reported having leftover medications in their home, most commonly antibiotics and opioids. Of respondents, 217/536 (40.5%) reported not knowing how to properly dispose of medications and only 88/535 (16.4%) reported receiving guidance regarding medication disposal. Most caregivers reported throwing leftover medications in the trash (55.7%) or flushing them down the toilet (38.5%). Caregivers with private insurance for their child were more likely to have leftover medications (adjusted odds ratio [aOR], 1.99; CI, 1.15-3.44), whereas Hispanic caregivers (aOR, 0.24; CI, 0.14-0.42) and those who received guidance on leftover medications (aOR, 0.30; CI, 0.11-0.81) were less likely to have leftover medications in the home. CONCLUSIONS: Leftover medications are commonly stored in homes with children and most caregivers do not receive guidance on medication disposal. Improved education and targeted interventions are needed to ensure proper medication disposal practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".