Bibliographic record
Abstract
Reid McDonald and Anita Cumbleton have updated me on their new pharmacy that opened in June in Cochrane, Alberta. While traditional measures of prescription volume suggest a slow start, clinical services have been booming. And that’s by design. Indeed, over two-thirds of their income is from provision of clinical services. A great start indeed! What is striking to me is how these practitioners are taking advantage of a number of opportunities for patient-centred care and using these to drive the volume of clinical services. So far, this includes a 24-hour ambulatory blood pressure monitoring service, partnering with a psychologist for a patient who does not have a family physician, partnering with a physiotherapist and participating in the RxEACH study (a trial of cardiovascular risk reduction by pharmacists). In partnering with nonphysician practitioners, Anita and Reid can use their prescribing authority and medication review skills to provide timely and accessible patient-centred care. Their biggest challenge has been awareness in the community. Despite radio and print advertising, many people in the community of Cochrane are not aware of their pharmacy and their services. My guess is that if they continue setting themselves apart the way they have been, the word will spread throughout the community and they will flourish. Meanwhile, in Thunder Bay, Bryan Gray has also remarked that the traditional prescription business has been growing slowly. In speaking to him about opportunities, he says, “As pharmacists, we are sometimes too focused on our own profession. In order to collaborate, we should also learn about the practice and funding models of other clinicians—this will help us to ‘fit in’ with them.” Indeed, he has created an opportunity by offering his services to the nurse practitioners in 2 aboriginal health clinics, a day per week in each. Asked how he got in to these clinics, he states that he just “cold called” and offered his services. That is a bold approach that we need to see more often. In this case, he realized that primary care team members are salaried and get paid a flat fee to take care of patients. So, by understanding that payment model, he realized that offering his services would result in less work for them, increasing their capacity and range of services. Another opportunity that Bryan has been taking advantage of is Ontario’s Pharmaceutical Opinion program. He and his staff have been proactively reviewing patient profiles for drug interactions and drug therapy problems (using Lexi-Drugs to help). While pharmaceutical opinions pay much less than MedsCheck reviews, he says that his revenue from pharmaceutical opinions is actually higher than that from MedsChecks because patients can have more than one per year. It helps him to detect real or potential problems for his patients. Brilliant! At the risk of repeating myself, you can see a theme building here. It’s about looking for and seeking out opportunities to improve primary care. It’s time for pharmacists to be proactive and bold. ■
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.023 | 0.024 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.097 | 0.044 |
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".