Loneliness in Australian older adults with mental illness
Bibliographic record
Abstract
Background: Older adults with mental illnesses are particularly vulnerable to the impact of loneliness. Despite this, loneliness is not regularly screened for in mental health services and remains under-recognized and under-treated. This research examines the prevalence of loneliness (overall, emotional, and social) in community-dwelling older adults with mental illness in an urban Australian setting and its relationship with depression and anxiety. Methods: A cross-sectional survey methodology was used to assess the point prevalence of loneliness in older adults (65+ years) with mental illness accessing an older adult mental health service. Four questionnaires were administered to examine loneliness (De Jong Gierveld Loneliness Scale), depressive symptoms (Geriatric Depression Scale – Short Form), anxiety symptoms (Geriatric Anxiety Inventory) and cognition (Montreal Cognitive Assessment). Data were analysed using correlation and linear regression. Loneliness was dichotomized based on clinical thresholds to understand the effect of loneliness on anxiety and depressive symptoms. Results: 54.1% of respondents reported loneliness, and emotional loneliness was more prevalent than social loneliness. There was a moderate, positive correlation between overall loneliness and depressive and anxiety symptoms. Both emotional and social loneliness were also associated with clinically significant depressive and anxiety symptoms. Conclusion: More than half of older adults with mental illness experience loneliness. Given known and well-researched associations between loneliness and poor physical and mental health, we advocate that routine screening of loneliness is relevant for this vulnerable group. Through prompt recognition, effective bespoke interventions targeted at loneliness, such as intergenerational groups, could be introduced by nurses to improve physical and mental well-being, quality of life and aid recovery.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".