Reuse of unused medications: a cross-sectional study on public willingness and influencing factors
Bibliographic record
Abstract
Medication waste is a significant global concern with environmental, economic, and healthcare implications. In Saudi Arabia, approximately 25.8% of dispensed medications are wasted, resulting in an annual cost of $150 million. Re-dispensing unused medications has been proposed to reduce this waste; however, its feasibility depends on public acceptance, regulatory frameworks, and assurances of safety. This study aimed to assess the Saudi public's willingness to accept re-dispensed medications returned unused to pharmacies and to identify factors influencing this willingness. A descriptive cross-sectional survey was conducted online across Saudi Arabia. The questionnaire, adapted from a validated tool by McRae et al. McRae et al. (Pharmacy (Basel), 2021, 9(2): 77) and translated into Arabic, explored demographics, medication practices, storage and disposal, and attitudes towards medication waste and re-dispensing. The survey was distributed via social media. Data were analyzed using SPSS version 29, including chi-squared tests and binary logistic regression. A total of 405 participants completed the survey, primarily female (64%) and aged 25-44 years (43%). About 64% reported having unused medications at home, most commonly stored in bedrooms (55.1%) and kitchens (53.6%). Disposal practices included keeping medicines for future use (62.5%), discarding them with household waste (45.7%), sharing them with others (21.5%), and returning unused medications to a pharmacy (8.4%). Approximately 60% were willing to accept re-dispensed tablets and 55% capsules, whereas fewer accepted other dosage forms. Key factors influencing acceptance included pharmacist verification of quality and integrity (79.3%), informed consent (77.3%), expiry dates (77%), and intact packaging (74.8%). Most participants (68.1%) indicated they would return unused medicines if a re-dispensing program were implemented, and half (50.6%) believed all medications, not only expensive ones, should be considered. Significant predictors of willingness included age (P < 0.001), employment status (P = 0.004), regular prescription use (P = 0.046), and concern about waste (P < 0.001). Younger participants showed higher acceptance, while employed individuals, retirees, and regular medication users were more hesitant. The findings indicate cautious yet notable public support for medication re-dispensing in Saudi Arabia, particularly for oral solid dosage forms, provided rigorous safety measures are assured. Policymakers should consider these insights to guide initiatives aimed at reducing medication waste.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".