Which ethical values underpin England’s National Health Service reset of paediatric and maternity services following COVID-19: a rapid review
Bibliographic record
Abstract
Objective To identify ethical values guiding decision making in resetting non-COVID-19 paediatric surgery and maternity services in the National Health Service (NHS). Design A rapid review of academic and grey literature sources from 29 April to 31 December 2020, covering non-urgent, non-COVID-19 healthcare. Sources were thematically synthesised against an adapted version of the UK Government’s Pandemic Flu Ethical Framework to identify underpinning ethical principles. The strength of normative engagement and the quality of the sources were also assessed. Setting NHS maternity and paediatric surgery services in England. Results Searches conducted 8 September–12 October 2020, and updated in March 2021, identified 48 sources meeting the inclusion criteria. Themes that arose include: staff safety; collaborative working – including mutual dependencies across the healthcare system; reciprocity; and inclusivity in service recovery, for example, by addressing inequalities in service access. Embedded in the theme of staff and patient safety is embracing new ways of working, such as the rapid roll out of telemedicine. On assessment, many sources did not explicitly consider how ethical principles might be applied or balanced against one another. Weaknesses in the policy sources included a lack of public and user involvement and the absence of monitoring and evaluation criteria. Conclusions Our findings suggest that relationality is a prominent ethical principle informing resetting NHS non-COVID-19 paediatric surgery and maternity services. Sources explicitly highlight the ethical importance of seeking to minimise disruption to caring and dependent relationships, while simultaneously attending to public safety. Engagement with ethical principles was ethics-lite, with sources mentioning principles in passing rather than explicitly applying them. This leaves decision makers and healthcare professionals without an operationalisable ethical framework to apply to difficult reset decisions and risks inconsistencies in decision making. We recommend further research to confirm or refine the usefulness of the reset phase ethical framework developed through our analysis.
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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 | 0.084 | 0.314 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.022 | 0.018 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".