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Enregistrement W4401593704 · doi:10.11124/jbies-24-00359

The time to act is now! The imperative of resident quality of life in long-term care

2024· article· en· W4401593704 sur OpenAlexaffabout
Matthias Hoben, Charlotte Berendonk

Notice bibliographique

RevueJBI Evidence Synthesis · 2024
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueIntergenerational Family Dynamics and Caregiving
Établissements canadiensYork UniversityUniversity of Alberta
Organismes subventionnairesnon disponible
Mots-clésTerm (time)Long-term careQuality (philosophy)BusinessPsychologyMedicineNursingPhilosophyEpistemologyPhysics

Résumé

récupéré en direct d'OpenAlex

For decades, older adults, their family or friend caregivers, advocates, and researchers have demanded that long-term care systems prioritize resident quality of life.1 Quality of life is a person’s perceived well-being, formed by complex interactions of physical, material, social, spiritual, and emotional components.1 Yet, long-term care systems commonly emphasize residents’ physical care and safety over quality of life.1 Three reviews2–4 published in this issue of JBI Evidence Synthesis are a testament to this problem. Robertson and colleagues’3 qualitative systematic review illustrates the devastating consequences of spousal separation. The admission of a person to residential long-term care, while their partner remains in the community, is a tragic example of how long-term care systems struggle to prioritize quality of life. Eligibility for publicly funded residential long-term care usually depends on the care needs of the person to be admitted. A partner with lower care needs is often denied admission. Therefore, very few long-term care residents live in the care home with their partner, increasing the risk of loneliness and depression for both the resident and their spouse. As the authors demonstrate,3 substituting the lost relationships is possible and may mitigate the negative effects. For example, community-dwelling spouses can volunteer or develop new routines, while both residents and spouses may benefit from humor and developing other meaningful relationships. However, the available evidence for these findings is weak, and avoiding the separation of couples in the first instance would seem more aligned with prioritizing quality of life. Indeed, health systems have demonstrated that spousal separation can be prevented. For example, the Canadian province of Nova Scotia has implemented the Life Partners in Long-Term Care Act,5 which enables placement of a couple at the highest level of care required by either of the two partners. Increasing supports in the community to enable aging in place for longer is another, frequently discussed option.6 Expanding services, such as adult day programs that support both the older adult and their family/friend caregiver, may be highly promising.7 However, research is lacking in this area, and even with strong community supports, older adults’ care needs often become too complex to be managed in the community.8 Therefore, residential long-term care remains an important component of older adult care,8 and health systems will have to prioritize resident quality of life. As Macdonald and colleagues4 point out in their mixed methods systematic review, one frequently promoted strategy to prevent or reduce loneliness and depression in long-term care residents is the use of assistive technologies that support social interactions (eg, phones, tablets, video games). The quantitative evidence is heterogeneous, based on a small number of studies, and does not suggest an effect of social technologies on social isolation and loneliness in this population. In contrast, qualitative studies report that interacting with family and friends via technology can bring residents joy, comfort, a feeling of connectedness, and increased well-being. However, technology use for older people can be challenging and they require assistance. Without assistance, these challenges can reduce the positive effects of technology. Two important issues must be considered in the context of using social technologies to improve residents’ quality of life. First, an intervention must be aligned with the resident’s individual needs and preferences.9 Not every long-term care resident will benefit from social technologies, and their use must include tailored activities to foster interactions based on each resident’s needs and preferences. Second, while largely supporting the use of social technologies, residents and family/friend caregivers have clearly emphasized that these technologies can enhance personal interactions but cannot replace them.10 The need to individually tailor interventions to long-term care residents’ needs and preferences is also highlighted in McArthur and colleagues’ systematic review and meta-analysis.2 Available evidence for the effectiveness of physical rehabilitation for improving long-term care residents’ physical functioning or quality of life was found to be of low certainty, the intervention dosage was deemed too low to affect physical functioning, and most interventions were not individually tailored. This absence of tailored interventions may be an important reason for the apparent lack of effectiveness for residents’ quality of life. For example, a resident who has always enjoyed physical workouts with weight training may enjoy and benefit from such activities, but other residents may prefer (and benefit more from) activities that involve games or even household chores. In conclusion, all 3 reviews in this issue demonstrate the dearth of high-certainty research on critical issues in long-term care. They also highlight how essential individually tailored approaches are—on a system-level and at the bedside. After decades of calls to prioritize long-term care resident quality of life, the time to act is now. We do need more research, but existing research clearly points to much-needed changes in our approach to care provided in this setting. Implementing those changes that align to resident quality of life must be our priority.

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 distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,003
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,695
Score d'incertitude au seuil0,997

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,017
Tête enseignante GPT0,351
Écart entre enseignants0,335 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2024
Routes d'admission2
Résumé présentoui

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