What does it take to change practice? Perspectives of pharmacists in Ontario
Bibliographic record
Abstract
BACKGROUND: This is a time of rapid change in the profession of pharmacy. Anecdotally, there are concerns that the pace, extent and rate of practice evolution are lagging. There is little evidence documenting the influencers and mechanisms that drive practice changes forward in pharmacy in Canada. METHODS: An exploratory qualitative method was selected, using both one-on-one interviews with self-categorized typical pharmacists and larger focus groups to provide context and confirmation of themes generated through interviews. Data were analyzed and coded using a constant-comparative iterative method, in order to generate themes related to the factors influencing pharmacists to actually change their practice. RESULTS: A total of 46 pharmacists meeting inclusion criteria participated in this study in focus groups, interviews or both. Nine themes were identified: 1) permission, 2) process pointers, 3) practice/rehearsal, 4) positive reinforcement, 5) personalized attention, 6) peer referencing, 7) physician acceptance, 8) patients' expectations and 9) professional identity supportive of a truly clinical role. One theme that did not emerge was payment, or remuneration, as a specific or isolated motivational factor for change. INTERPRETATION: The complexity of practice change in pharmacy and the multiple factors highlighted in this study point to a more deliberate and concerted effort being needed by diverse pharmacy organizations (educators, regulators, employers, professional associations, etc.) to support pharmacists through the change management process. CONCLUSIONS: The "9 Ps of practice change" identified through this study can provide pharmacists with guidance in terms of how to better support evolution of the profession in a more time-efficient and effective manner.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".