Speaking up in the NHS in England: the work of the National Guardian and NHS England
Bibliographic record
Abstract
The Francis Report following the Public Inquiry at Mid Staffordshire NHS Foundation Trust identified that workers had tried to speak up about patient safety concerns but had been ignored and even victimised as a result.1 In the subsequent Freedom to Speak Up Report, Sir Robert Francis made recommendations for the changes needed to improve the NHS, leading to an open and transparent culture for the benefit of patient care.2 Freedom to Speak Up builds on the work already undertaken in primary care in order to tackle and prevent patient safety incidents. Nearly a quarter of responders to the consultation were from primary care, demonstrating that there is still more to do.3 NHS England’s guidance requires all primary care providers to identify a Freedom to Speak Up Guardian either within or outside the organisation. This editorial describes the requirements for Freedom to Speak Up in primary care in England and how NHS organisations are developing a culture of safety and learning where all workers are able to speak up safely. The landscape of primary care is complex, with numerous smaller providers and some larger, multisite providers. Speaking up in small teams can be challenging particularly if your manager is also your employer, or for GPs, raising concerns about a partner or colleague. Regulations in primary care include the National Performers List regulations4 and Revalidation regulations5 which are the statutory responsibility of NHS England and the General Medical Council respectively. The commissioning landscape is complex and shifting, with CCGs taking on co-commissioning of general practice with NHS England. This raises the question — who do you turn …
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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 | 0.025 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.022 |
| Scholarly communication | 0.025 | 0.017 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".