Community pharmacists’ awareness, identification, and management of prescribing cascades: A cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: Prescribing cascades can lead to unnecessary medication use, healthcare costs, and patient harm. Pharmacists oversee prescriptions from multiple prescribers and are well positioned to identify such cascades, making pharmacists key stakeholders to address them. OBJECTIVES: To evaluate community pharmacists' awareness, identification, and management of prescribing cascades and to assess behavioural determinants that may be targeted in future strategies to minimise inappropriate prescribing cascades. METHODS: An online survey was developed using the Theoretical Domains Framework (TDF) and emailed to all registered community pharmacists in Ireland (n = 3775) in November 2021. Quantitative data were analysed using descriptive and inferential statistics. Free-text sections were given to capture reasons for non-resolution of identified prescribing cascades and suggestions to aid prescribing cascade identification and management; this text underwent content analysis. RESULTS: Of the 220 respondents, 51% were aware of the term 'prescribing cascade' before the survey, whilst 69% had identified a potentially inappropriate prescribing cascade in practice. Over one third were either slightly confident (26.4%) or not confident at all (10%) in their ability to identify potentially inappropriate prescribing cascades in patients' prescriptions before the survey, whilst 55.2% were concerned that patients were receiving prescribing cascades they had not identified. Most respondents wanted further information/training to help prescribing cascade identification (88.3%) and management (86.1%). Four predominant TDF domains identified were common to both i) influencing non-resolution of identified prescribing cascades and ii) in the suggestions to help identify and manage prescribing cascades: 'Environmental Context and Resources', 'Social/Professional Role and Identity', 'Social Influences' and 'Memory, Attention and Decision Processes'. CONCLUSIONS: There is a clear need to provide additional resources to help community pharmacists identify and manage prescribing cascades. These findings will support the development of theory-informed behaviour change strategies to aid the minimisation of inappropriate prescribing cascades and decrease the risk of medication-related harm for patients.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".