Insights into the implementation of a whole genome sequencing report form (SRF) to reduce nosocomial SARS-CoV-2 within UK hospitals: a qualitative process evaluation using normalisation process theory
Bibliographic record
Abstract
Abstract Background Here we report on a process evaluation conducted as part of a large multisite non-randomised trial of the effectiveness of a whole genome sequence report form (SRF) to reduce nosocomial SARS-CoV-2 through changing infection prevention and control (IPC) behaviours during the COVID − 19 pandemic. We detail how the SRF was implemented across a heterogeneous purposive sub-sample of hospital trial sites (n = 5/14). Methods We conducted in-depth interviews from diverse professional staff (N = 39). Inductive thematic analysis initially explored participants’ accounts of implementing the SRF. The resulting data driven themes, concerning the way the SRF was used within sites, were then coded in relation to the key tenets of normalisation process theory (NPT). Results Factors that enabled the implementation of the SRF included: elements of the context such as health care professional passion; the existence of whole genome sequencing (WGS) infrastructure; effective communication channels, the creation of new connections across professionals and teams; the integration of SRF-led discussions within pre-existing meetings and the ability of a site to achieve a rapid turnaround time. In contrast, we found factors that constrained the use of the SRF included elements of the context such as the impact of the Alpha-variant overwhelming hospitals. In turn, dealing with COVID-19 breached the limited capacity of infection prevention and control (IPC) to respond to the SRF and ensure its routinisation. Conclusion We show preliminary support for the SRF being an acceptable, useable and potentially scalable way of enhancing existing IPC activities. However, the context of both the trial and the alpha wave of COVID-19 limit these insights. Clinical trial number https://www.isrctn.com/ISRCTN50212645, Registration date 20/05/2020
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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 | 0.176 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| 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".