‘<i>I think we could probably do more</i>’: an interview study to explore community pharmacists’ experiences and perspectives of frailty and optimising medicines use in frail older adults
Bibliographic record
Abstract
BACKGROUND: Community pharmacists potentially have an important role to play in identification of frailty and delivery of interventions to optimise medicines use for frail older adults. However, little is known about their knowledge or views about this role. AIM: To explore community pharmacists' knowledge of frailty and assessment, experiences and contact with frail older adults, and perceptions of their role in optimising medicines use for this population. METHODS: Semi-structured interviews conducted between March and December 2020 with 15 community pharmacists in Northern Ireland. Interviews were transcribed verbatim and analysed thematically. RESULTS: Three broad themes were generated from the data. The first, 'awareness and understanding of frailty', highlighted gaps in community pharmacists' knowledge regarding presentation and identification of frailty and their reluctance to broach potentially challenging conversations with frail older patients. Within the second theme, 'problem-solving and supporting medication use', community pharmacists felt a large part of their role was to resolve medicines-related issues for frail older adults through collaboration with other primary healthcare professionals but feedback on the outcome was often not provided upon issue resolution. The third theme, 'seizing opportunities in primary care to enhance pharmaceutical care provision for frail older adults', identified areas for further development of the community pharmacist role. CONCLUSIONS: This study has provided an understanding of the views and experiences of community pharmacists about frailty. Community pharmacists' knowledge deficits about frailty must be addressed and their communication skills enhanced so they may confidently initiate conversations about frailty and medicines use with older adults.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".