Uptake and impact of regulated pharmacy technicians in Ontario community pharmacies
Bibliographic record
Abstract
BACKGROUND: Since 2010, most provincial Colleges of Pharmacists have licensed pharmacy technicians. The colleges hoped this would give pharmacists time to provide "expanded scope" activities such as medication reviews. Little is known, however, about the uptake and impact of pharmacy technicians on pharmacists' provision of such services. We address these questions using data for Ontario community pharmacies. METHODS: Data on pharmacists and pharmacy technicians were obtained from the Ontario College of Pharmacists website in September 2016. Their place of employment was used to calculate the number of full-time equivalent (FTE) pharmacists and technicians employed at each community pharmacy. Pharmacy claims data for the 12-month period ending March 31, 2016, were obtained from the Ontario Public Drug Programs (OPDP). These data included number of MedsChecks performed, type of MedsCheck and number of prescriptions dispensed to OPDP beneficiaries. RESULTS: Pharmacy technicians were employed in 24% of the pharmacies in our sample. Technician employment rates were highest in Central Fill pharmacies and pharmacies serving long-term care facilities. In general, pharmacies employing 1 or fewer technician full-time equivalents (FTEs) had a slightly higher probability of providing MedsChecks and, of those that did provide Meds Checks Annuals, provided more of them. Pharmacies that hired 3 or more technician FTEs were markedly less likely to provide MedsChecks. CONCLUSIONS: Pharmacies differ in their employment of technicians and in the apparent impact of technicians on the provision of MedsChecks. However, these represent associations. Additional research is needed to assess the causal effect of technician employment on the provision of MedsChecks.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".