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Record 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 on OpenAlexaffabout
Matthias Hoben, Charlotte Berendonk

Bibliographic record

VenueJBI Evidence Synthesis · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsYork UniversityUniversity of Alberta
Fundersnot available
KeywordsTerm (time)Long-term careQuality (philosophy)BusinessPsychologyMedicineNursingPhilosophyEpistemologyPhysics

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.695
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.351
Teacher spread0.335 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations2
Published2024
Admission routes2
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