Living MedsCheck
Bibliographic record
Abstract
OBJECTIVE: To share the experiences of graduating students as they learn to deliver a new medication review service in community pharmacies in Ontario, Canada. PRACTICE DESCRIPTION: Four graduating pharmacy students volunteered in different community pharmacies to learn how to navigate a new provincial program called MedsCheck, which pays pharmacists to do medication reviews. Each student selected his or her own practice site, including 2 independent community pharmacies, a grocery store chain pharmacy and a hospital outpatient pharmacy. PRACTICE INNOVATION: To help the students learn to deliver the new MedsCheck services, a faculty mentor met with them on a weekly basis. To reflect on doing MedsChecks in the "real world" and to elicit feedback from the online community, each student blogged about his or her experiences. RESULTS: All 4 students felt that peer mentoring improved their ability to deliver MedsCheck services. They also identified a number of barriers to delivering the MedsChecks and helped each other try to overcome the barriers. CONCLUSION: MedsCheck is a new service in Ontario and is not easily implemented in the current pharmacy model of practice. Peer mentoring is a helpful way to share successes and overcome barriers to delivery. Can Pharm J 2013;146:33-38.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.075 | 0.009 |
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".