Bibliographic record
Abstract
Objective: To provide and measure the effects of having a resource pharmacist committed to the front store for his or her work shift. Clients could use this service to access individualized, timely, professionally provided information to guide them in their health choices. Methods: A resource pharmacist was scheduled to be located in the front of the store for his or her whole shift. An immediate record of interventions was kept in a pocket notebook, with more in-depth recording done later on a standardized form. Clients were greeted by the pharmacist and counted, with a goal of showcasing pharmacists' accessibility. Clients who required counselling were counted separately and categorized. Our highest number day had 82 clients greeted and 32 counselled — a total of 114 clients; our lowest had 15 greeted and 10 counselled — a total of 25 clients. Promotion, signage and mailout were done to increase awareness of the new service. The key message promoted was of the benefits of a one-on-one consult with the resource pharmacist on the client's health concerns and counselling for over-the-counter medication. Results: This has proven to be a successful professional service that can be easily implemented into any community pharmacy. Professional satisfaction with the process was high. The dispensing pharmacist could focus on prescriptions, while the resource pharmacist was able to provide a patient-centred and individualized service. The clients had ready access to information that clarified their choices; gave them immediate, individualized health information; and sometimes ended with a referral to other health care providers. An example of some of the health issues addressed were cancer, suicide, depression and various chronic illnesses. The resource pharmacist was able to provide a service that will increase the efficiency of the health care system by empowering clients to take charge of their health and making professionally advised decisions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.283 | 0.040 |
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".