Learning Needs of Pharmacists for an Evolving Scope of Practice
Bibliographic record
Abstract
Around the world, changes in scope of practice regulations for pharmacists have been used as a tool to advance practice and promote change. Regulatory change does not automatically trigger practice change; the extent and speed of uptake of new roles and responsibilities has been slower than anticipated. A recent study identified 9 pre-requisites to practice change (the 9Ps of Practice Change). The objective of this study was to describe how educationalists could best apply these 9Ps to the design and delivery of continuing professional development for pharmacists. Twenty community pharmacists participated in semi-structured interviews designed to elicit their learning needs for scope of practice change. Seven supportive educational techniques were identified as being most helpful to promote practice change: (i) a coaching/mentoring approach; (ii) practice-based experiential learning; (iii) a longitudinal approach to instructional design; (iv) active demonstration of how to implement practice change; v) increased focus on soft-skills development; (vi) opportunities for practice/rehearsal of new skills; and (vii) use of a 360-degree feedback model. Further work is required to determine how these techniques can be best applied and implemented to support practice change in pharmacy.
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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".