Factors influencing communication and decision-making about life-sustaining technology during serious illness: a qualitative study
Notice bibliographique
Résumé
OBJECTIVES: We aimed to identify factors influencing communication and decision-making, and to learn how physicians and nurses view their roles in deciding about the use of life-sustaining technology for seriously ill hospitalised patients and their families. DESIGN: The qualitative study used Flanagan's critical incident technique to guide interpretive description of open-ended in-depth individual interviews. SETTING: Participants were recruited from the medical wards at 3 Canadian hospitals. PARTICIPANTS: Interviews were completed with 30 healthcare professionals (9 staff physicians, 9 residents and 12 nurses; aged 25-63 years; 73% female) involved in decisions about the care of seriously ill hospitalised patients and their families. MEASURES: Participants described encounters with patients and families in which communication and decision-making about life-sustaining technology went particularly well and unwell (ie, critical incidents). We further explored their roles, context and challenges. Analysis proceeded using constant comparative methods to form themes independently and with the interprofessional research team. RESULTS: We identified several key factors that influenced communication and decision-making about life-sustaining technology. The overarching factor was how those involved in such communication and decision-making (healthcare providers, patients and families) conceptualised the goals of medical practice. Additional key factors related to how preferences and decision-making were shaped through relationships, particularly how people worked toward 'making sense of the situation', how physicians and nurses approached the inherent and systemic tensions in achieving consensus with families, and how physicians and nurses conducted professional work within teams. Participants described incidents in which these key factors interacted in dynamic and unpredictable ways to influence decision-making for any particular patient and family. CONCLUSIONS: A focus on more meaningful and productive dialogue with patients and families by (and between) each member of the healthcare team may improve decisions about life-sustaining technology. Work is needed to acknowledge and support the non-curative role of healthcare and build capacity for the interprofessional team to engage in effective decision-making discussions.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».