A multifaceted intervention to reduce antimicrobial prescribing in care homes: a non-randomised feasibility study and process evaluation
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,051 | 0,052 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,003 | 0,004 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,005 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».