A multifaceted intervention to reduce antimicrobial prescribing in care homes: a non-randomised feasibility study and process evaluation
Bibliographic record
Abstract
Background The most frequent acute health-care intervention that care home residents receive is the prescribing of medications. There are serious concerns about prescribing generally, and about antimicrobial prescribing in particular, with facilities such as care homes being described as an important ‘reservoir’ of antimicrobial resistance. Objectives To evaluate the feasibility and acceptability of a multifaceted intervention on the prescribing of antimicrobials for the treatment of infections. Design This was a non-randomised feasibility study, using a mixed-methods design with normalization process theory as the underpinning theoretical framework and consisting of a number of interlinked strands: (1) recruitment of care homes; (2) adaptation of a Canadian intervention (a decision-making algorithm and an associated training programme) for implementation in UK care homes through rapid reviews of the literature, focus groups/interviews with care home staff, family members of residents and general practitioners (GPs), a consensus group with health-care professionals and development of a training programme; (3) implementation of the intervention; (4) a process evaluation consisting of observations of practice and focus groups with staff post implementation; and (5) a survey of a sample of care homes to ascertain interest in a larger study. Setting Six care homes – three in Northern Ireland and three in the West Midlands. Participants Care home staff, GPs associated with the care homes and family members of residents. Interventions A training programme for care home staff in the use of the decision-making algorithm, and implementation of the decision-making algorithm over a 6-month period in the six participating care homes. REACH (REduce Antimicrobial prescribing in Care Homes) Champions were appointed in each care home to support intervention implementation and the training of staff. Main outcome measures The acceptability of the intervention in terms of recruitment, delivery of training, feasibility of data collection from a variety of sources, implementation, practicality of use and the feasibility of measuring the appropriateness of prescribing. Results Six care homes from two jurisdictions were recruited, and the intervention was adapted and implemented. The intervention appeared to be broadly acceptable and was implemented largely as intended, although staff were concerned about the workload associated with study documentation. It was feasible to collect data from community pharmacies and care homes, but hospitalisation data from administrative sources could not be obtained. The survey indicated that there was interest in participating in a larger study. Conclusions The adapted and implemented intervention was largely acceptable to care home staff. Approaches to minimising the data-collection burden on staff will be examined, together with access to a range of data sources, with a view to conducting a larger randomised study. Trial registration Current Controlled Trials ISRCTN10441831. Funding This project was funded by the National Institute for Health Research (NIHR) Health Services and Delivery Research programme and will be published in full inHealth Services and Delivery Research; Vol. 8, No. 8. See the NIHR Journals Library website for further project information. Queen’s University Belfast acted as sponsor.
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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 | 0.051 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".