Strategies to Improve Antimicrobial Resistant Organism Admission Screening in a Provincial Healthcare System: Consensus-Based Approach
Notice bibliographique
Résumé
Background: Adherence with antimicrobial resistant organism (ARO) admission screening is suboptimal, despite clinical support tools in clinical information systems (CIS) to facilitate the process. Behaviour change techniques to improve adherence are needed. However, in a resource-constrained healthcare system, strategies that motivate healthcare workers (HCWs) to align their practices with infection prevention and control (IPC) policies need to be prioritized. Methods: An online survey (REDCap) and a virtual (Zoom) consensus meeting using a modified nominal group technique with online voting was conducted among HCWs, IPC, and the CIS staff in September and October 2024, respectively, to achieve consensus on a prioritized list of interventions to improve ARO admission screening at acute care and acute rehabilitation facilities (n=100) in Alberta, Canada. Interventions from the Behaviour Change Wheel were mapped to barriers/enablers influencing screening adherence. Each intervention was judged across the APEASE criteria (Acceptability, Practicality, Effectiveness, Affordability, Side Effects, Equity) using a 5-point Likert Scale. Consensus to include interventions required >4 criteria with >80% agreement, consensus to exclude required >4 criteria with 80%. Interventions that did not reach consensus were discussed to determine whether to include in the final candidate list. Attendees were asked to vote on their top three interventions from the final candidate list. Results: There were 15 barriers and one enabler to ARO admission screening, mapped to 43 unique interventions. Of these, 16 interventions addressed more than one barrier/enabler, while 27 interventions only addressed a single barrier. Fifty-nine respondents completed the survey. Most respondents (63%) were IPC staff, 20% were nurses, and 17% were other HCWs (including IPC physicians). Nine interventions met criteria to include in the candidate list, 26 were excluded, and 8 interventions did not reach consensus in the survey and were discussed. There were 32 attendees at the consensus meeting (53% IPC staff and physicians, 34% clinical staff, 13% other provincial teams). Three interventions were selected: 1) creating a nursing task to complete the tool in the CIS when an admission order is signed, 2) add a banner on the CIS Storyboard when the tool is not complete, and 3) develop a best practice guideline for frontline staff on ARO admission screening. Conclusions: The survey and consensus meeting were efficient methods to determine a prioritized list of interventions, which will be implemented and evaluated, to improve ARO admission screening in Alberta.
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 | 0,074 | 0,082 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,009 | 0,006 |
| Études des sciences et des technologies | 0,006 | 0,003 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,007 | 0,010 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
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.
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 ».