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.’ The judging panel made the following comments regarding the paper: ‘Highly topical area of research which provides good underpinning evidence to take forward the sustainability agenda where medicines waste has a big impact; Excellent SRL with risk of bias assessment. Followed PRISMA guidelines. Prospero registered. Good example of a systematic review; very likely to inform future research and practice in this area’. ‘Interesting topic and very relevant as climate and environmental issues are of significant concern and large numbers of people are on medication, study is well done, though unfortunate the geographic representation is limited in studies included, this paper may start important conversations on medication take back programs in community pharmacy and primary care.’ The judging panel were impressed by the quality and importance of the shortlisted papers and reflecting this the following four were awarded ‘Highly Commended’: Gender and ethnicity bias in generative artificial intelligence text-to-image depiction of pharmacists by Geoffrey Currie et al. (https://doi.org/10.1093/ijpp/riae049) [4] This study demonstrated the gender and ethnic biases in the representation of pharmacists in images generated by DALL-E 3 via GPT-4. ‘DALL-E 3 disproportionately represents pharmacists as predominantly lightskin toned men which does not represent the diversity of Australian pharmacists.’ Knowledge, attitudes, and practices of community pharmacists providing over-the-counter emergency hormonal contraception: a scoping review by Beverley D. Glass et al. (https://doi.org/10.1093/ijpp/riae062) [5] The scoping review highlights the need to improve pharmacists training while delivering emergency hormonal contraception. ‘Pharmacists displayed positive attitudes but remained conservative about supplying ECPs to adolescents and third parties.’ The role of pharmacists in community palliative care—a scoping review by Thilini R. Thrimawithana et al. (https://doi.org/10.1093/ijpp/riae015) [6] This review demonstrates the need to include pharmacists in community palliative care teams to deliver ‘medication reviews, provision of education to patients and other healthcare professionals and ensure timely access to palliative care medicines.’ Patient perspectives on the vital primary care role of community pharmacists in Nova Scotia, Canada: qualitative findings from the PUPPY Study by Emily G. Marshall et al. (https://doi.org/10.1093/ijpp/riae008) [7] This study summarizes the positive patient perspectives regarding ‘primary care provided by community pharmacists in Nova Scotia’ and expanding their scope of practice. The shortlisted papers, selected on the basis of objective criteria, serendipitously represent an interesting snapshot of pharmacy practice research today. Three of the papers report systematic reviews of the literature, conducted well, in accordance with an established method. Given the rapidly expanding volume of published research a systematic assessment of what is already known on a topic is an essential first step to undertake before embarking on new research. One of the other two papers is based on observational data, demonstrating a structured and reproducible approach to selecting cases to evaluate against a set of predetermined characteristics. The other is an in-depth qualitative interview study, reported well using the COREQ checklist. The topics across the five papers are also of interest highlighting how pharmacists can contribute to environmental sustainability, support patients at the end of life, improve sexual health, confirm patients’ positive views on extending role of pharmacists in delivering front line care, and provide objective evidence on why we should be aware of some of the caveats associated with AI. We thank the authors of these papers, and indeed authors of all our published papers, for pushing back the boundaries of knowledge and supporting better patient care. Zita Zachariah: Conceptualization and led the writing of this Editorial, Christine Bond: Conceptualization and substantive contribution to the writing. Both authors approved the final version. Zita Zachariah is Managing Editor of the Royal Pharmaceutical Society portfolio of research journals and Christine Bond is Editor-in-Chief of the International Journal of Pharmacy Practice. They managed the shortlisting of the top papers applying objective criteria and oversaw the final selection process but did not vote. This Editorial has not been peer reviewed. None declared.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".