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Enregistrement W3007186279 · doi:10.3310/hsdr08080

A multifaceted intervention to reduce antimicrobial prescribing in care homes: a non-randomised feasibility study and process evaluation

2020· article· en· W3007186279 sur OpenAlex
Carmel Hughes, David R Ellard, Anne Campbell, Rachel Potter, Catherine Shaw, Evie Gardner, Ashley Agus, Dermot O’Reilly, Martin Underwood, Mark Loeb, Bob Stafford, Michael M. Tunney

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Notice bibliographique

RevueHealth Services and Delivery Research · 2020
Typearticle
Langueen
DomaineHealth Professions
ThématiquePatient Satisfaction in Healthcare
Établissements canadiensMcMaster University
Organismes subventionnairesHealth Services and Delivery Research ProgrammeQueen's UniversityNational Institutes of HealthMedical Research CouncilPublic Health AgencyQueen Mary University of LondonArthritis AustraliaQueen's University BelfastUniversity of SouthamptonNational Institute for Health and Care ResearchNational Institute for Health and Care ExcellenceEconomic and Social Research CouncilWorld Health Organization
Mots-clésMedicinePsychological interventionIntervention (counseling)Focus groupNursingHealth careFamily medicine

Résumé

récupéré en direct d'OpenAlex

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 in Health 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.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,005
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,049
Score d'incertitude au seuil0,996

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0050,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,297
Tête enseignante GPT0,551
Écart entre enseignants0,254 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle