Implementation of the COVID-19 antiviral therapy Nirmatrelvir/Ritonavir (PaxlovidTM) across Canada in 2022: A qualitative analysis of key facilitating factors and challenges
Bibliographic record
Abstract
Background: , N/R) was approved for use in Canada in January 2022, with the Government of Canada assuming a procurement role and provinces, territories, and federal departments implementing usage within their respective healthcare systems. The objective of this analysis is to describe how N/R was implemented across various jurisdictions in the first six months after it was available for use and identify promising implementation practices. Methods: Fourteen semi-structured discussions in small group settings were conducted with jurisdictional representatives involved in the implementation of N/R. A descriptive analysis of the eligibility criteria and service delivery model was conducted. A thematic analysis using the Consolidated Framework for Implementation Research and cluster analysis of the codes were then undertaken on NVivo 12 to identify key themes. Results: Overall, the eligibility criteria were similar across jurisdictions, and three types of service delivery models were identified. Ten main themes emerged as facilitators and eight as challenges to the implementation. Partnership, collaboration, communication and flexibility were among the facilitators identified, while the complexity of the intervention (e.g., drug-drug interactions), perceived evidence gaps in effectiveness by prescribers, and resource limitations were identified as key implementation challenges. Conclusion: While there were jurisdictional variations in the implementation of N/R, communication and collaboration, and the availability of rapid testing for COVID-19 emerged as key facilitators. Drug-drug interactions, resource pressures and limited evidence were some of the key challenges. Overall, these facilitators and challenges were similar across jurisdictions and may help inform future therapeutic implementation plans for pandemic preparedness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".