Facilitating integration of regulated pharmacy technicians into community pharmacy practice in Ontario: Results of an exploratory study
Bibliographic record
Abstract
Background:The integration of regulated pharmacy technicians (RPTs) into community pharmacy practice was intended to relieve pharmacists of certain technical duties to facilitate greater provision of direct patient care services, commensurate with expanded scope of practice. There is scant data available regarding the success, value and impact of RPT integration, either in Canada or in other jurisdictions.Methods:Pharmacists and RPTs working in community practices were interviewed. Qualitative data were categorized using an iterative coding process to identify themes related to barriers and facilitators to integrating and optimizing the role of the RPT in community practice in Ontario.Results:A total of 16 RPTs and 12 pharmacists were interviewed from community sites in Ontario. Strategies for facilitating successful integration of RPTs into daily workflow were identified, based on 4 major themes: environmental factors, interpersonal factors, professional identity formation and innovative use of delegation.Interpretation:Integration of RPTs into community practice is complex and requires careful management, planning, training and follow-up to ensure attainment of objectives. Simply hiring RPTs and placing them into existing workflow patterns is generally not a successful implementation strategy.Conclusions:Implementation strategies identified through this study can provide employers, managers, pharmacists and RPTs with opportunities to enhance RPT integration and optimize the role of both pharmacists and RPTs in community practice.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".