Developing and validating a competency framework for advanced pharmacy practice
Bibliographic record
Abstract
AIM. To develop and validate an advanced practice competency framework. DESIGN. Literature review and expert panel discussions. SUBJECTS AND SETTING. Consensus panel membership drawn from across the NHS. Framework mapped against the practice of leading-edge practitioners drawn from primary care and national clinical pharmacy groups. RESULTS. From a literature review 34 competencies were identified and grouped into 6 competency domains. Consensus development panels validated the descriptor terms used to define competency at "foundation", "excellence", or "mastery" level practice. 28 (of the 35 surveyed) practitioners mapped their practice using the framework. The majority indicated that their practice was at "mastery" for the "expert practice" and "building relationships" clusters, although a broader level of activity was reported for the other four clusters. CONCLUSIONS. This study developed an evidence-based advanced practice competency framework, grounded in the multi-disciplinary literature and validated by expert opinion. This provides a map of the key generic skills, knowledge and attributes required by individuals practising at this higher level. The competencies and descriptors developed by this research could be used as a template for the development of consultant pharmacists.
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 | 0.085 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".