Symposia — Conferences — Oral Communications
Notice bibliographique
Résumé
Background: A growing number of older people reside in long-term care (LTC) homes.As they near the end-of-life, it is vital that LTC residents express their healthcare wishes though Advance Care Planning (ACP).Yet, ACP remains suboptimal and LTC residents often experience unmet needs and unnecessary hospital transfers.Objectives: We applied the Knowledge-To-Action framework to 1) identify shared barriers and solutions to improve the process of ACP and end-of-life care for LTC residents; 2) develop a standardized, scalable, and person-centered approach to ACP, and 3) evaluate this approach in a multicentre cluster randomized trial.Methods: We began in September 2017 with a 1-day workshop for 44 LTC stakeholders, including residents and families, from Manitoba, Alberta, and Ontario.Sessions were recorded and thematic analysis performed.An environmental scan was conducted to assess ACP practices in 38 LTC homes in participating provinces.Over the following 11 months, we developed the intervention to address weak links in ACP.From August 2018 to August 2020, we conducted an unblinded, cluster-randomized, mixed-methods trial in 29 LTC homes (15 intervention, 14 control) in these provinces to assess the impact of the intervention on ACP comprehensiveness and care and interventions at the end-of-life (ClinicalTrials.govNCT03649191).Results: ACP challenges include: 1) differing provincial ACP frameworks; 2) lacking clarity on substitute decision maker (SDM) identity and role; 3) failing to share sufficient information when residents formulate care wishes; and 4) failing to communicate during a health crisis.The environmental scan identified that most conduct ACP upon resident admission, with 90% repeating these when resident clinical status changes.Residents are often excluded from ACP. Physician involvement is often very limited, even in emergencies, leading to decisions counter to resident wishes.Recognizing the variability in physician involvement in ACP, we designed BABEL to be delivered by nurses.Requiring approximately 60 minutes, BABEL: 1) confirms the identity and role of the SDM; 2) prepares the SDM for medical emergencies; 3) explains the resident's clinical situation and prognosis; 4) ascertains the resident's decision-making philosophy; and 5) identifies preferred treatment options for medical emergencies most likely to be faced by that resident.Intervention materials include: a workbook, training tools for LTC staff, and knowledge tools for all stakeholders.The workbook contains carefully worded scripts to guide staff on helping residents and families navigate sensitive ACP.A preliminary discussion is intended to take place very soon after LTC admission, followed 2-8 weeks later by the Full BABEL Discussion, which is the core of the intervention.The trial recruited 713 LTC residents aged >= 65 years with an elevated risk of dying within the next year.The intervention significantly increased the comprehensiveness of ACP.Comfort in dying did not differ between groups.Antimicrobial use was significantly lower in intervention homes.Conclusions: The superior comprehensiveness of a person-centered BABEL ACP, codesigned with LTC stakeholders, underscores the importance of allowing adequate time for these discussions, to address all the important aspects of ACP, and may reduce unwanted interventions at the end of life.
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 | 0,005 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,337 | 0,169 |
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; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».