Bibliographic record
Abstract
I RECENTLY HAD THE GOOD FORTUNE TO PARTICIPATE IN A SPECIAL Pharmacy Symposium organized by the School of Pharmacy at Queen’s University Belfast. The Chair of CPJ’s Editorial Advisory Board, Dr. Ross Tsuyuki, and I were among several international speakers invited to present highlights of current initiatives from our respective jurisdictions. I really enjoyed hearing presentations detailing the interesting practice research initiatives underway in different settings in Northern Ireland, including a study of pharmacists working with physicians to improve prescribing of psychoactive drugs in nursing homes. Our colleagues there are also investigating factors influencing overthe-counter recommendations made by pharmacists, and how pharmacist interventions can improve the cost-effectiveness of treatments for peptic ulcer disease. But despite their innovative research on topics like medication adherence in children with chronic diseases, community pharmacists in Northern Ireland still experience familiar frustrations such as too much time spent on dispens
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.015 | 0.019 |
| Insufficient payload (model declined to judge) | 0.027 | 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".