Bibliographic record
Abstract
My recent career change was initially a source of worry. I was making a huge transition: leaving my practice as a staff pharmacist in a family-based community pharmacy to start a new practice as a pharmacy manager in a clinic that specializes in drug addiction and pain management. The clinic offered an inter-collaborative practice where the pharmacy team works with the physicians to manage patients' addictions and pain so that they can lead productive lives. In addition to working closely with physicians, I would have the opportunity to develop personal relationships with patients who receive methadone on a weekly or even daily basis. I would be focusing on medications that I had never dispensed before, with patients who are typically stereotyped as being “difficult.” The job was associated with a mix of pros and cons, but overall, the opportunity was unique and its appeal prompted me to take on the challenge. I am thrilled to say that I made a very fulfilling career decision. My relationship with my patients extends beyond their medications, as they often share their ups and downs with the pharmacy team. The working relationship that I have with the physicians is critical in managing patients' pain, drug addiction and the adverse effects from their medications. The physicians are just an office door (or a Blackberry message) away and they are truly dedicated to resolving any medication issues that may arise. This practice environment is also the perfect opportunity for pharmacists to use their skills and offer pharmaceutical opinions, which aligns well with the Ontario government's novel reimbursement of expanded services. You are entitled to a professional fee if you make an intervention at the time of dispensing that requires a physician consult for a patient who is an Ontario Drug Benefit recipient.1 Based on the conversation with the physician, you can choose either to not dispense, dispense as prescribed or adjust the prescription.1 Working closely with methadone and Suboxone, I make sure that patients have a valid prescription for only 1 dose per day (prescriptions with duplicate start dates may be overlooked and the patient may be double-dosed) and that the prescription extends until the patient's next visit with his or her physician (so that the patient does not go into withdrawal). If there is therapeutic duplication or therapy needed, I make an intervention and consult the physician. I also monitor patients' doses to make sure that any increases or decreases are by an appropriate amount and consult the physician if I have a concern. Not only has my new position been a delightful change in environment, but it has allowed me to be part of moving the profession forward to focus on our cognitive utility in medication management. Through this experience, I have learned that even in different practice environments, there is always opportunity to exercise your knowledge and make a difference. I'm looking forward to more expanded services for pharmacists!
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.534 | 0.218 |
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".