Trajectories of Depression and Their Predictors in a Population-Based Study of Korean Older Adults
Bibliographic record
Abstract
The aim of this study was to determine trajectories of depression in older adults and to identify predictors of membership in the different trajectory groups. A total of 3983 individuals aged 65 or older were included. Latent class growth models were used to identify trajectory groups. Of 3983 individuals, 2269 (57%) were females, with a mean baseline age of 72.4 years ( SD = 6 years). Four depression trajectories were identified across 8 years of follow-up: “low-flat” ( n = 3636; 86.6%), “low-to-middle” ( n = 214; 9.2%), “low-to-high” ( n = 31; 1.3%), and “high-stable” ( n = 102; 2.9%). Compared to the low-flat depression group, high-stable depression group members were more likely to be female, have three or more chronic diseases, and were more likely not to own a home. Our findings will assist health policy decision-makers in planning intervention programs targeting those most likely to experience persistent depression in order to improve psychological well-being in the elderly.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".