Integrating a Brief Pharmacist Intervention into Practice: Osteoporosis Pharmacotherapy Assessment
Bibliographic record
Abstract
Pharmacy practice is in the midst of a change that is both necessary and long overdue. This change is driven by evidence that medication mismanagement and preventable adverse drug events occur at an alarming rate and the fact that pharmacists have the potential to positively impact these patient outcomes if their expertise were fully utilized.1–3 Unfortunately, some pharmacist interventions proven effective in the literature have not been integrated into practice. For example, the SCRIP study was terminated early after the intervention was associated with improvements in cholesterol management, deeming it unethical not to treat the control group.4 Despite this evidence, the SCRIP intervention has not been widely implemented. Several barriers that limit the integration of new interventions have been identified, including lack of time, disruption of workflow, requirement of additional training and lack of reimbursement. 5–8 Lengthy or intensive pharmacist interventions are particularly difficult to integrate, as they are impacted by all of these barriers. Alternatively, brief and focused interventions may be more readily integrated into practice. Unfortunately, we identified no examples in the literature describing these types of brief interventions. The purpose of this paper is to provide an example of how a brief intervention can be integrated into a contemporary pharmacist practice without disrupting workflow or requiring training, by describing an intervention that was piloted at West Winds Primary Health Centre in Saskatoon, Saskatchewan. The goal is not to evaluate this intervention, but rather to use it as an illustration. Ethics approval was unnecessary, as no formal evaluation was performed and no patient data were used.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".