Strategies to promote guideline adoption: lessons learned from the implementation of a national COVID-19 hospital guideline across NHS Wales
Bibliographic record
Abstract
There is little understanding about what proportion of the target audience have read guidelines published through the traditional approach. The COVID-19 pandemic created a particularly difficult scenario for healthcare professionals (HCP) since the evidence base rapidly changed. In response, we established a freely accessible, video-based online resource, which was formally implemented requiring user registration. The guideline rapidly gained more than 4,500 registrants in the first wave alone, including nearly 100% of respiratory, intensive care or emergency unit consultants in Wales. During the first wave, there were nearly 170,000 page views with over 31,000 video plays and an average of 5.8 visits to the site per registrant. Acceptability using an online survey showed widespread support and that the unsubscribe rates were remarkably low. We suggest that this novel approach to guideline implementation achieved its aim of widespread engagement and acceptability and serves as a potential model for future medical guidelines and education beyond COVID-19.
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 | 0.101 | 0.185 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".