C-reactive protein diagnostic test uptake in primary care: a qualitative study of the UK’s 2019–2024 AMR National Action Plan and lessons learnt from Sweden, the Netherlands and British Columbia
Bibliographic record
Abstract
OBJECTIVES: This study compares the implementation of C-reactive Protein (CRP) testing to distinguish between acute bacterial and viral infections in primary care in three WHO European region countries (Sweden, the Netherlands and the UK) and one WHO Americas region country (Canada) to generate insights that health services can use to guide decision-making on CRP test implementation. SETTING: Three settings were included: (1) national-level policy interviewees in England, (2) front-line antibiotic prescribers operating in primary care in England and (3) national-level policy interviewees in Canada, Sweden and the Netherlands. PARTICIPANTS: We conducted in-depth qualitative interviews with 35 UK antimicrobial resistance policy-makers, clinicians and allied medical professionals. We also interviewed 13 national-level diagnostic test experts in Canada, Sweden, the Netherlands and the UK. We transcribed, thematically coded and analysed the data, using the NASSS framework for analysing technology-supported healthcare change. RESULTS: There were multiple barriers and facilitators to CRP test implementation. The Netherlands was seen as the country with the most successful implementation of CRP, with enabling IT, financing and quality assurance infrastructure alongside strong guideline adherence to prevent overtesting. By contrast, Swedish interviewees reported 'CRP-itis', or widespread, guideline-discordant testing. UK uptake, contingent on local-level commissioning, has stalled amid mixed professional views. Canadian professional views are sceptical, and uptake plans are nearly non-existent. The NASSS themes emerging as the strongest facilitator or barrier to diagnostic uptake were the organisation and wider systems, with some reflections emerging on the limitations of NASSS to capture interface issues emerging from the analysis. CONCLUSIONS: Health services considering CRP test adoption in primary care need to be mindful that, where concerns about the value proposition of the technology exist among policy-makers and professionals, CRPs are unlikely to be implemented; where there are discordant practices between policymakers/guidelines and clinicians, adoption of CRP testing may increase both antibiotic prescribing and costs to the health system. Where there is local-level commissioning of diagnostic tests, uptake may stagnate; and where implementation succeeds, it may be to the detriment of the public purse.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 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".