A scoping review of influencing factors associated with loneliness in nursing home settings
Bibliographic record
Abstract
OBJECTIVES: Older adults residing in nursing home (NH) settings are at risk of loneliness, a complex and multi-dimensional concept that adversely affects health. To effectively identify and address loneliness, it is crucial to enhance the understanding of the social, psychological, cultural, and environmental factors associated with loneliness. METHOD: A scoping review was conducted to examine the current evidence. Thematic analysis was employed to identify factors influencing (dimensions of) loneliness, which were ranked on proximity to loneliness. RESULTS: Based on 38 research papers, 27 factors related to loneliness were identified, of which 13 were sufficiently representative. A minority of these factors were directly linked to the social, emotional and existential dimensions of loneliness. The most frequently mentioned factors were categorised into the domains of health and functioning, stress-coping, social contact, and relationship quality. CONCLUSION: Loneliness is best addressed within the immediate social environment of NH settings, and staff play a crucial role in fostering a sense of belonging. Moreover, residents' experiences and needs regarding loneliness and its dimensions must be thoroughly assessed and discussed, particularly for those suffering from dementia. Furthermore, our review emphasises the urgent need for comprehensive, inclusive, and consistent research on loneliness in NH settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".