Strategies supporting sustainable prescribing safety improvement interventions in English primary care: a qualitative study
Bibliographic record
Abstract
BACKGROUND: While the use of prescribing safety indicators (PSI) can reduce potentially hazardous prescribing, there is a need to identify actionable strategies for the successful implementation and sustainable delivery of PSI-based interventions in general practice. AIM: To identify strategies for the successful implementation and sustainable use of PSI-based interventions in routine primary care. DESIGN & SETTING: Qualitative study in primary care settings across England. METHOD: Anchoring on a complex pharmacist-led IT-based intervention (PINCER) and clinical decision support (CDS) for prescribing and medicines management, a qualitative study was conducted using sequential, multiple methods. The methods comprised documentary analysis, semi-structured interviews, and online workshops to identify challenges and possible solutions to the longer-term sustainability of PINCER and CDS. Thematic analysis was used for the documentary analysis and stakeholder workshops, while template analysis was used for the semi-structured interviews. Findings across the three methods were synthesised using the RE-AIM (reach, efficacy, adoption, implementation, and maintenance) framework. RESULTS: Forty-eight documents were analysed, and 27 interviews and two workshops involving 20 participants were undertaken. Five main issues were identified, which aligned with the adoption and maintenance dimensions of RE-AIM: fitting into current context (adoption); engaging hearts and minds (maintenance); building resilience (maintenance); achieving engagement with secondary care (maintenance); and emphasising complementarity (maintenance). CONCLUSION: Extending ownership of prescribing safety beyond primary care-based pharmacists, and achieving greater alignment between general practice and hospital prescribing safety initiatives, is fundamental to achieve sustained impact of PSI-based interventions in primary care.
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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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".