Co-development of a community pharmacy training regarding fentanyl and xylazine test strips
Bibliographic record
Abstract
Introduction: Fentanyl and xylazine test strips (FTS, XTS) are simple point-of-care tests that determine the presence of fentanyl or xylazine in a substance before use. Access to FTS and XTS is limited. For pharmacists who are willing to sell an FTS, there is little guidance about how to implement FTS sales and counseling as no training for community pharmacists regarding FTS and XTS exists. This article describes how a FTS and XTS training for community pharmacists was co-designed. Methods: A co-design strategy was utilized that involved an advisory panel of eight members: three practicing community pharmacists, two harm reduction experts, a website developer, the director of practice advancement for the state pharmacy association, and a patient-provider communication expert. A total of six meetings occurred to develop the training over seven months from July 2023 to February 2024. The advisory panel met once a month to discuss training goals, develop training information, and revise and structure the training to ensure the acceptability and appropriateness of the training for North Carolina community pharmacists. Results: The co-design strategy led to the development of a 6-module 30-min training. Module topics included information that stakeholders felt was most important to include: (1): What and Why of Test Strips, (2) Why pharmacies? (3) How to use/ "Best practices of testing" (4) Logistics (5) FAQs and (6) Resources. Panelists determined an online self-paced webinar would be most useful for pharmacists to reference when needed. Conclusion: The inclusion of stakeholders, including product end-users, leads to the creation of content that is salient and feasible for pharmacists to implement, which may increase their ability to integrate a new pharmacy service (FTS and XTS sales and counseling) into their pharmacy workflow. Patient or public contribution: This training was developed through a co-design strategy for community pharmacists with community pharmacist input. This training also utilized feedback from harm reduction experts who have trained people who use drugs on the best practices of testing their substances with FTS and XTS. The incorporation of their feedback was integral to the development of this training and will ensure that the training is feasible for the pharmacist to integrate into their workflow.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".