Bibliographic record
Abstract
As one of the most accessible primary health care providers, pharmacists are in an excellent position to provide primary health care services. Although slow, the current transition from a dispensing-centered (transaction-based) pharmacy approach to a patient-centered (patient care based) approach is important in addressing the primary healthcare needs of Canadians. This is especially true in patients with chronic conditions, as they will visit the pharmacy more frequently to retrieve their prescriptions. As such, pharmacists have multiple opportunities to review their condition, adjust accordingly, and help patients achieve their health goals both pharmacologically and non-pharmacologically. Yet, this is dependent on the pharmacist being proactive in managing their patient’s health, as many patients do not know how or when to ask for help. The first chapter of this thesis introduces the types of primary care services pharmacists can provide that was investigated. It provides an outline of the following chapters and a rationale behind the importance and purpose of the investigations conducted. The second chapter of this thesis investigates the effects of an innovative workflow model that places pharmacists at the front, allowing for immediate patient – pharmacist interaction, and observe the effects that this type of proactive workflow model has on managing hypertension and diabetes. In chapters three and four, we address how pharmacists are able to play a role in combating the Canadian opioid epidemic. Naloxone, an opioid antagonist that is the primary treatment for opioid poisoning, is an effective tool that even the general public would be able to administer to someone experiencing opioid poisoning. However, distribution of this crucial life-saving tool through pharmacies is haphazard across Canada. In chapter three, we investigate the current disparity in naloxone access in community pharmacies throughout Canada. Integrating these findings in chapter four, we provide recommendations for pharmacists on how and when they can proactively dispense naloxone to the general public.
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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 | 0.010 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".