Effects of prescription adaptation by pharmacists
Bibliographic record
Abstract
BACKGROUND: Granting dispensing pharmacists the authority to prescribe has significant implications for pharmaceutical and health human resources policy, and quality of care. Despite the growing number of jurisdictions that have given pharmacists such privileges, there are few rigorous evaluations of these policy changes. This study will examine a January 2009 policy change in British Columbia (BC), Canada that allowed pharmacists to independently adapt and renew prescriptions. We hypothesize this policy increased drug utilization and drug costs, increased patient adherence to medication, and reduced total healthcare resource use. METHODS/DESIGN: We will study a population-based cohort of approximately 4 million BC residents from 2004 through 2010. We will use data from BC PharmaNet on all of the prescriptions obtained by this cohort during the study period, and link it to administrative billings from physicians and hospital discharges. Using interrupted time series analysis, we will study longitudinal changes in drug utilization and costs, medication adherence, and short-term health care use. Further, using hierarchical modelling, we will examine the factors at the regional, pharmacy, patient, and prescription levels that are associated with prescription adaptations and renewals. DISCUSSION: In a recent survey of Canadian policymakers, many respondents ranked the issue of prescribing privileges as one of their most pressing policy questions. No matter the results of our study, they will be important for policymakers, as our data will make policy decisions surrounding pharmacist prescribing more evidence-based.
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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 | 0.005 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".