Community pharmacists’ perspectives on assessing kidney function and medication dosing for patients with advanced chronic kidney disease: A qualitative study using the theoretical domains framework
Bibliographic record
Abstract
Background: The kidneys are responsible for the elimination of many drugs. Chronic kidney disease (CKD) is common, and medications may require adjustment to avoid adverse outcomes. Despite the availability of kidney drug dosing resources, people with CKD are at risk of inappropriate drug prescribing. Community pharmacists are in the ideal position to mitigate harm from inappropriate prescribing in this population. Methods: In this qualitative study, community pharmacists were interviewed on their perspective on kidney function assessment and dose adjustment in people with advanced CKD (estimated glomerular filtration rate <30 mL/min/1.73 m 2 ). The theoretical domains framework for targeting behavioural change was used to inform the interview guide and analysis. Purposeful sampling was employed until data saturation. Semistructured virtual interviews were audio-recorded, transcribed verbatim and uploaded into NVIVO 12 Pro to facilitate thematic analysis. Deductive and inductive iterative coding approaches were employed to determine categories and themes. Results: Twelve pharmacists were interviewed, with a mean age of 42 years and 16 years of experience. Four themes comprising 10 categories were identified to influence kidney function assessment and dosing, including resources (information access, technology, references), environment (pharmacy infrastructure, practice setting), reflection (triggers, experience and training, collaboration) and leadership and governance (pharmacist role, advocacy). Feedback on an optimal CKD tool was collected, and enabling themes (categories) for implementation included knowledge and skills (education, training) and reflection (role, support, integration). Conclusions: Findings will inform the interventions needed to improve implementation of kidney assessment and dosing of high-risk medications in people with kidney impairment into community pharmacy practice. Can Pharm J (Ott) 2023;156:xx-xx.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".