Assessing quality in community pharmacy
Bibliographic record
Abstract
Abstract Objective This review aimed to identify English-language instruments used to assess quality in community pharmacy and to evaluate their reported validity, reliability, feasibility and acceptability. Method A systematic review was conducted to identify literature relating to the use of instruments to assess quality in community pharmacy. The electronic databases searched included Embase, International Pharmaceutical Abstracts, Medline, e-PIC and Pharmline, covering the period of time between January 1990 and March 2007. Reference lists of identified studies and websites of pharmacy bodies were also searched. Key findings Ten instruments were identified from Canada, Malta, the UK and the US. These were used for quality-assurance and/or quality-improvement purposes and focused on: clinical governance systems; organisational culture/maturity; safety (climate and systems); effectiveness of pharmacy services; and stakeholders' feedback on services. The assessments were at different stages of development, and the majority had not been tested for construct validity, reliability and feasibility. Conclusions Assessments with high validity and reliability give a good indication of the quality of care provided and can indicate areas for improvement. Further research is needed to establish a composite view of quality in community pharmacy; and many of the instruments identified required validation.
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 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 | 0.024 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".