Factors associated with the implementation and adoption of point-of-care diagnostic tests to detect antimicrobial resistance: A qualitative study in Quebec, Canada
Bibliographic record
Abstract
Background: Antimicrobial resistance (AMR) represents a major public health concern worldwide. To effectively combat AMR, the use of point-of-care (POC) diagnostic tests is recommended by the World Health Organization (WHO). In this qualitative study, we investigated the drivers that influence the implementation and adoption of POC diagnostic tests in healthcare settings in Quebec, Canada, to help fight against AMR. Methods: Interviews were conducted with experts on AMR and/or diagnostic tests at the federal and provincial (Quebec) levels. Applying Greenhalgh and colleagues’ non-adoption, abandonment, scale-up, spread, and sustainability (NASSS) framework as a theoretical basis, we examined the complexities involved in implementing diagnostic innovations aimed at reducing AMR. Results: A total of 42 participants were interviewed. We identified multiple drivers across the development, assessment and implementation stages of new POC tests: the complexities associated with evolving AMR and POC technology development; issues related to trust in test results; challenges of cost-benefit analyses; considerations regarding user impact; local organizational aspects related to POC tests; the regulatory, political, and economic contexts; and the impact of the COVID-19 pandemic on public health priorities. Conclusion: The implementation of diagnostic tests that deliver rapid results to inform antibiotic prescription is a priority in Canada and globally. However, our study underscores the complexity and challenges involved in adopting new POC tests. Despite presenting challenges, the COVID-19 pandemic has also facilitated the development and assessment of diagnostic innovation in healthcare settings. Our study further emphasizes the need for AMR to be elevated as a political priority for effective management.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".