Evaluation of Point-of-Care Testing in Pharmacy to Inform Policy Writing by the New Brunswick College of Pharmacists
Bibliographic record
Abstract
Pharmacy practice continues to advance, allowing professionals to contribute further to patient care and the healthcare system. Pharmacists are authorized to perform point-of-care testing (POCT) in seven out of ten Canadian provinces. In considering the potential for enhanced clinical decision-making with the opportunity to gain patient data at the site of care, the New Brunswick College of Pharmacists (NBCP) proceeded to draft regulatory amendments and a policy to enable POCT scope in New Brunswick. Policy writing is a core function of Provincial Regulatory Authorities in Canada as the process determines principles that direct pharmacy practice. Each province has a differing scope of practice and method for developing documents. This paper highlights the approach, analysis, and findings of the NBCP pursuant to drafting a POCT policy. The policy development process included a literature search and environmental scan of the ten Canadian provincial regulatory authorities along with other countries. The findings highlighted in this paper describe the use of POCT, quality assurance, regulatory framework, educational opportunities, and the role of pharmacy technicians in relation to POCT in a pharmacy setting. The approach NBCP took to engage professionals and decisions on the direction of the policy are described. As point-of-care services continue to expand in pharmacies, the insights by the NBCP can be utilized by other regulatory bodies or pharmacy professionals who are implementing or enhancing POCT policies or procedures within their organizations.
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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 | 0.228 | 0.346 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".