Dual trajectories of loneliness and depression and their baseline correlates over a 14‐year follow‐up period in older adults: Results from a nationally representative sample in Taiwan
Bibliographic record
Abstract
AIMS: To explore the codevelopment between loneliness and depression in older adults, and to identify its potential baseline individual, family and extrafamilial correlates. BACKGROUND: The number of older adults around the world has steadily increased over the last decades. Later life is a particularly vulnerable life stage due to multiple unfavourable conditions, and mental health in this stage appears to become an inescapable issue. Previous research has found the cross-sectional association between loneliness and depression, but their codevelopment has been understudied. Therefore, exploring the codevelopment and its correlates has significant implications for prevention and healthcare professionals. DESIGN: A longitudinal follow-up study. METHODS: The study used nationally representative data over a 14-year follow-up period from the Taiwan Longitudinal Study on Ageing focused on Taiwanese aged 60 years and above (n = 4049). Group-based trajectory modelling, group-based dual-trajectory modelling and multinomial logistic regression were the primary analytical methods. RESULTS: We identified three distinct dual trajectories of loneliness and depression: longitudinal low-frequency lonely depressed (29.3%), longitudinal moderate-frequency lonely depressed (59.4%) and longitudinal high-frequency lonely depressed (11.3%). After considering several demographic and background characteristics, difficulty in physical functioning, number of physical symptoms and diseases, sleep quality and number of child deaths were found to be significantly associated. CONCLUSION: Across the three identified dual-trajectory groups, they all showed a stable loneliness frequency pattern over time; however, the moderate-frequency group and high-frequency group both had a trajectory of increasing depression. It seems that depression tends to change over time in a worsening direction, especially for those with a certain frequency of loneliness. Furthermore, differences in individual and family correlates were found across the groups. IMPLICATIONS FOR PRACTICE: Interventions focusing on the specific factors may help hinder coexisting loneliness and depression, and have implications for developing health promotion strategies and chronic disease care plans.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".