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Record W4416057512 · doi:10.1093/ijpp/riaf093.072

(ID: 220) Dispensing practices for oral liquid medicines: a survey of UK community pharmacies

2025· article· en· W4416057512 on OpenAlexaff
M Tylek, M Punton, M Sadr-Kazemi, Stephen Tomlin, Mandy Wan

Bibliographic record

VenueInternational Journal of Pharmacy Practice · 2025
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical studies and practices
Canadian institutionsNorthlands College
Fundersnot available
KeywordsPharmacyCommunity pharmacyMedical prescriptionDescriptive statisticsAlternative medicinePharmacy practiceComputer-assisted web interviewing

Abstract

fetched live from OpenAlex

Abstract Introduction In the UK, community pharmacies dispense over one billion prescription items per year, playing an integral role in the safe and effective use of medicine [1]. Their involvement is thus crucial in shaping medicine-related practice standards to drive system-wide improvements in medication safety. Published studies have shown that oral liquid medicines present particular challenges, including the availability of multiple concentrations of the same medicine, variations in how doses are expressed on dispensing labels (e.g. ml, ml(mg), spoonful), and the need for patients or caregivers to measure doses accurately [2–4]. While these findings have prompted efforts to develop professional standards for dispensing such medicines, there is limited insight of how these medicines are dispensed in community pharmacy settings. Aim To explore current dispensing practices for oral liquid medicines in community pharmacies. Methodology An anonymous, self-administered online survey consisting of 8 questions was conducted over an 8-week period between January and March 2025. The survey was distributed to community pharmacy staff through the Community Pharmacy Patient Safety Group, a network hosted by the Company Chemists’ Association. The group collectively represents both large chains and independent community pharmacies. Data were analysed using R and summarised using descriptive statistics. Ethical approval was not required. Results The 237 respondents were based across 53 integrated care/ health boards. Among all respondents, 76% (179/237) reported that doses of oral liquid medicines were ‘almost always’ or ‘frequently’ expressed in millilitres on prescriptions, while 45% (106/237) indicated that spoonful was also commonly used. Pharmacies stocked a median of five dosing device types (range: 2–7), with availability ranging from 24% to 98%. The 2.5 mL syringe was least available, and the 5 mL syringe most common. When supplying dosing devices, 67% (159/237) of respondents indicated that they ‘often’ or ‘always’ check patients’ or carers’ preferences. Scenario-based questions showed variations in practice. In the 5.7 mL dose scenario, 12 variations in response were reported. In the scenarios where manufacturer-supplied dosing devices lacked the accuracy required for the prescribed dose, a range of responses were also noted in how these devices were managed and in the provision of suitable alternatives. In response to the question on excipient content in oral liquid medicines, 45% (107/237) of respondents indicated a need for more educational resources or guidance. Discussion In community pharmacies, prescription dosing is more commonly expressed in volume, while hospitals typically use dose-based units like milligrams [5]. The study found variation in dosing device availability and use, highlighting potential need to standardise professional practices. Any future standardisation in practice would also need to align with patient understanding of dosage instructions to avoid subsequent administration errors [3]. While some differences may reflect efforts to offer patients choices, the potential of unintended impact of practice variation warrants further exploration. Although the study relied on self-reported data, and interpretation of scenarios may have varied among respondents, the findings highlight an opportunity to improve patient safety through the development of system-wide standards informed by an integrated perspective of hospital and community pharmacy practices.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.040
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.815
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.040
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.252
GPT teacher head0.555
Teacher spread0.303 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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