P-2013. Improving Access to <i>COVID-19</i> Treatment for Longterm Care (LTC) Residents: An Integrated Operational Approach
Notice bibliographique
Résumé
Abstract Background Residents residing in Long-term care (LTC) facilities suffer disproportionately from severe outcomes of COVID-19. Antiviral treatment remains underutilized despite ongoing evidence in reducing severe disease in this high-risk group. In this quality improvement study conducted in British Columbia, Canada, barriers to treatment access were evaluated and addressed with educational resources to support the implementation of protocols within the LTC setting. Concern regarding COVID-19 outbreaks in Longterm Care Bar Chart categorizing pre- and post-responses from LTC staff regarding the concern of COVID-19 outbreaks at their facility during the study period. Methods Educational resources were developed by the study team and disseminated to Long-term care staff (n=564). Specifically, information for patients and their families about COVID-19 antiviral treatment, care plan templates for residents who opt in for treatment, and digital education videos for staff to support implementation of the aforementioned resources within the LTC setting. During the study period, January to April 2024, LTC staff were surveyed to determine the the usefulness of the resources. Comfort level managing drug-drug interactions Bar Chart categorizing pre- and post-responses reflecting LTC staff comfort levels in managing drug interaction with PAXLOVID therapy before and after the intervention. Results LTC staff respondents (n=78) consisted of Nurses (n=54), Pharmacists (n=13), and other staff (n=11) across LTC facilities (n=43) in Vancouver, BC. Most respondents (n= 31) expressed extreme concern for COVID-19 outbreaks in their facility, which remained unchanged during the study period (p=0.848). They reported unclear patient eligibility and drug-drug interactions as the biggest barriers to accessing COVID-19 treatment for their residents. After reviewing all educational materials with their respective facilities, respondents showed statistically significant changes in their comfort levels in discussing COVID-19 treatment with residents and their families (p< 0.01). However, no statistically significant change was seen in the pre-and post-evaluation in comfort levels managing drug interactions with COVID-19 antiviral therapy (p=0.155). Comfort level discussing PAXLOVID treatment Bar Chart categorizing pre- and post-responses reflecting LTC staff comfort levels in discussing PAXLOVID therapy with residents and their families Conclusion COVID-19 remains a concern in LTC settings, with staff encountering barriers to accessing antiviral treatment for their residents. An integrated operational approach, defined as curated resources highlighting information for residents and their families and care plan templates to support implementation, may help support timely access to antiviral treatments in this setting. Perceived Barriers to COVID-19 treatment in LTC Bar Chart categorizing pre- and post-responses reflecting LTC staff perceived barriers in accessing COVID-19 treatment, in the event of a confirmed positive test Disclosures Ajit Johal, BSP BCPP RPh, GSK: Grant/Research Support|GSK: Honoraria|Merck: Grant/Research Support|Merck: Honoraria|Moderna: Honoraria|Pfizer: Grant/Research Support|Pfizer: Honoraria|Sanofi Pasteur: Honoraria|Seqirus: Honoraria|Valneva: Honoraria
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 | 0,017 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».