The ethics of setting national antibiotic policies using financial incentives
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is an increasingly urgent global public health issue. Data from Public Health England — from the English Surveillance Programme for Antimicrobial Utilisation and Resistance (ESPAUR) — quantifies the scale of antibiotic resistance in key bacterial pathogens. The Department of Health’s 5-year strategy to reduce morbidity and mortality associated with AMR (2013–2018) focused on optimising antibiotic prescribing and improving infection prevention and control.1 In April 2015 NHS England introduced a Quality Premium (QP) focusing on reducing antibiotics. QPs are financial rewards, with a maximal value equivalent to £5 per patient, intended to reward clinical commissioning groups (CCGs) for improvements in the quality of the services that they commission and for associated improvements in health outcomes and reducing inequalities. The AMR QP provided commissioners with financial incentives to reduce antibiotic prescribing; 80% were linked to primary care quality measures (reduction in absolute number of antibiotic prescriptions by 1%, decrease in use of broad spectrum antibiotics by 10%) and 20% linked to improving availability of antibiotic prescribing data from secondary care.2 Incentives are a tool that governments use to help support behaviour change, are a recognised domain in behaviour change methodology, and can be considered a form of trade. CCGs are offered an incentive in the form of additional funds for investment if they have reduced antibiotic prescribing. However, the CCG also has to show that it manages public funds responsibly and will only receive a QP if it has managed its funds according to the ‘Managing Public Money’ guidelines and does not require financial support during the financial year (nor deviate substantially from expected surpluses/deficits).2 In 2014/2015 only 27% of the total available QP was achieved by CCGs. Although the financial incentive is directed towards CCGs, the behavioural change being targeted is at the level of …
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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 | 0.136 | 0.243 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.020 | 0.019 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".