The Caregiver Pathway, a Model for the Systematic and Individualized Follow-up of Family Caregivers at Intensive Care Units: Development Study
Notice bibliographique
Résumé
BACKGROUND: Family caregivers of patients who are critically ill have a high prevalence of short- and long-term symptoms, such as fatigue, anxiety, depression, symptoms of posttraumatic stress, and complicated grief. These adverse consequences following a loved one's admission to an intensive care unit (ICU) are also known as post-intensive care syndrome-family. Approaches such as family-centered care provide recommendations for improving the care of patients and families, but models for family caregiver follow-up are often lacking. OBJECTIVE: This study aims to develop a model for structuring and individualizing the follow-up of family caregivers of patients who are critically ill, starting from the patients' ICU admission to after their discharge or death. METHODS: The model was developed through a participatory co-design approach using a 2-phased iterative process. First, the preparation phase included a meeting with stakeholders (n=4) for organizational anchoring and planning, a literature search, and interviews with former family caregivers (n=8). In the subsequent development phase, the model was iteratively created through workshops with stakeholders (n=10) and user testing with former family caregivers (n=4) and experienced ICU nurses (n=11). RESULTS: The interviews revealed how being present with the patient and receiving adequate information and emotional care were highly important for family caregivers at an ICU. The literature search underlined the overwhelming and uncertain situation for the family caregivers and identified recommendations for follow-up. On the basis of these recommendations and findings from the interviews, workshops, and user testing, The Caregiver Pathway model was developed, encompassing 4 steps: within the first few days of the patient's ICU stay, the family caregivers will be offered to complete a digital assessment tool mapping their needs and challenges, followed by a conversation with an ICU nurse; when the patient leaves the ICU, a card containing information and support will be handed out to the family caregivers; shortly after the ICU stay, family caregivers will be offered a discharge conversation by phone, focusing on how they are doing and whether they have any questions or concerns; and within 3 months after the ICU stay, an individual follow-up conversation will be offered. Family caregivers will be invited to talk about memories from the ICU and reflect upon the ICU stay, and they will also be able to talk about their current situation and receive information about relevant support. CONCLUSIONS: This study illustrates how existing evidence and stakeholder input can be combined to create a model for family caregiver follow-up at an ICU. The Caregiver Pathway can help ICU nurses improve family caregiver follow-up and aid in promoting family-centered care, potentially also being transferrable to other types of family caregiver follow-up.
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 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».