The <i>International Journal of Pharmacy Practice</i> paper of the year 2024 award
Bibliographic record
Abstract
The inaugural International Journal of Pharmacy Practice (IJPP) Paper of the Year 2024 Award was presented to the paper titled ‘The knowledge, attitude, and practice of the public regarding household pharmaceutical waste disposal: a systematic review (2013–2023)’ by Sheng Yuan Hiew and Bee Yean Low from the School of Pharmacy, University of Nottingham Malaysia, Malaysia [1]. This Award aims to recognize the most significant work published in the IJPP in the preceding year. Papers are shortlisted based on quantitative metrics such as article views, Altmetric scores, usage, and citations and then voted on by a judging panel comprising the eight Associate Editors of the journal based on the following criteria: topicality and originality, appropriateness of methodology and rigor of method, and potential to influence policy and/or professional practice/future research approaches. The UN Sustainable Development Goals have included ensuring availability and sustainable management of water and sanitation for all (Goal 6). Given that the improper disposal of unused/expired medicines contaminates soil and water detrimentally affecting plant and wildlife [2, 3], this timely systematic review synthesizes the results of 12 international studies to demonstrate the relationships between the current knowledge, attitudes, and practice of the public and household medication disposal. These findings could inform suitable interventions and strategies to implement safe medication disposal practices. The research identified a discrepancy between the knowledge and attitudes of the public regarding medication disposal with actual practice. Although the public were aware of the hazards posed by unused/expired medication and were positive regarding the need for safe disposal, binning in household trash and flushing down sinks and toilets remained the most prevalent method of disposal. The study recommended that ‘a multi-faceted approach that addresses knowledge gaps, reinforces positive attitudes and promotes an accessible medication take-back service can collectively safeguard public health and the environment.’
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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 | 0.029 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.116 | 0.032 |
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".