Non-Linear Relationships of Loneliness with Age in People Living with Schizophrenia and Non-Psychiatric Comparison Participants: Relations non linéaires entre la solitude et l’âge chez les personnes atteintes de schizophrénie et les personnes n’ayant aucun antécédent de maladie psychiatrique
Bibliographic record
Abstract
OBJECTIVE: Loneliness - distress that arises from discrepancies between perceived and desired relationships - is increasingly prevalent and recognized as a major public health concern due to the association with negative health outcomes. People living with schizophrenia (PLWS) experience higher rates of loneliness than the general population and may be particularly vulnerable to these adverse outcomes. In the general population, loneliness fluctuates throughout the lifespan, but the relationship between loneliness and age in PLWS is not well understood. METHOD: 271 adults, 141 adults with a diagnosis of schizophrenia or schizoaffective disorder (PLWS)and 130 adults with no history of major psychiatric illness (NCs) aged 27-69 completed clinical interviews and self-report measures assessing loneliness, perceived social support, and mental and physical health. Participants also completed blood draws for biomarkers of inflammation and hyperglycaemia. Locally Weighted Scatterplot Smoothing (LOWESS) regression modelling was used to examine potential non-linear relationships between loneliness and age for both groups and to select the polynomial that best fit the observed relationship. RESULTS: We observed an age by diagnostic group interaction (log estimate = -0.005, SE = 0.003) such that PLWS reported higher loneliness scores compared to NCs of similar age. Patterns of loneliness differed with age between diagnostic groups such that loneliness remained relatively stable and high for PLWS while for NCs loneliness increased from age 40 to age 60. In both groups, loneliness was associated with worse self-reported physical health, depression, and, among PLWS, positive symptoms. CONCLUSION: Results suggest different patterns of loneliness across adulthood for PLWS and NC, reflecting the different social milestones for NCs during this age period that are not as commonly experienced by PLWS, such as marriage, empty nesting and retirement. Loneliness is linked with poor physical and mental health outcomes among PLWS and may be an important target for improving morbidity and mortality for PLWS.Plain Language Summary TitleLoneliness and Aging in Schizophrenia.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".