Opportunities and challenges associated with the launch of nirmatrelvir/ritonavir (Paxlovid) in British Columbia during the COVID-19 pandemic: A qualitative study to explore community pharmacists’ perspectives
Bibliographic record
Abstract
Background: In February 2022, a novel antiviral for the treatment of COVID-19, nirmatrelvir/ritonavir (Paxlovid), was approved by Health Canada and made available to patients in British Columbia (BC). BC community pharmacists did not prescribe nirmatrelvir/ritonavir, but dispensing involved a detailed assessment with close attention to drug-drug interactions and patient monitoring. As the nirmatrelvir/ritonavir service was unique in BC, and not all pharmacists participated in the program, it is important to evaluate the perspectives of all the pharmacists who were affected so that lessons learned from the program can inform future pandemic planning and government initiatives. Methods: A qualitative research study using key informant semistructured interviews was conducted. Community pharmacists with varying degrees of experience with nirmatrelvir/ritonavir were recruited using multiple methods of recruitment through e-mail and fax invitations and social media posts. Open-ended questions explored pharmacists' experiences with the nirmatrelvir/ritonavir program, including barriers and facilitators to dispensing it, and recommendations for future pharmacy initiatives. Results: Forty-three community pharmacists participated in the study. Most participants were between the ages of 30 and 39 years and had practiced for less than 10 years. Thematic analysis yielded 36 codes that were organized into 3 overarching themes related to the following: learning; in-process experiences, supports, difficulties; and perceptions about the expanded scope of practice. Discussion: The following strategies may be helpful to consider including in future initiatives: preprinted forms, a hotline for peer support, slower rollouts, a single source for communicating changes, a patient portal, addressing the divisions between dispensary and clinical work, and having specialized positions to support future rollouts. Conclusion: Lessons learned from the nirmatrelvir/ritonavir service included a slower approach to program initiation with additional training opportunities and more streamlined prescription options. Pharmacists also wished for better communication support and involvement in the planning of future initiatives.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".