Prescribing by Pharmacists and Collaborative Care: Are We Ready to Accept the Baton and Get in the Race?
Bibliographic record
Abstract
We recently had the misfortune to come across the adverting campaign released by the Ontario Medical Association (OMA) that suggested, with some clever rhetoric, that physicians should be the only health care providers to prescribe drugs. 1 This advertisement was accompanied by a picture of a presumed patient with hands raised in front of his or her face, as if the person was to be the next victim in a teenage slasher movie. It is quite simply a new iteration of an old argument: there is no evidence that prescribing by pharmacists benefits patients. However, does the absence of evidence equate with the absence of benefit? Or is it simply that we have never measured outcomes? Pharmacists now have the opportunity to obtain additional prescribing authority in Alberta, which permits initial-access prescribing and prescribing in the management of chronic diseases, based on independent assessments of patients or the referral of patients from other health care professionals. 2,3 But are hospital pharmacists ready?
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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 | 0.020 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.016 | 0.016 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".