Six-month trajectories of self-reported depressive symptoms in long-term care
Bibliographic record
Abstract
BACKGROUND: Depression is a common problem in long-term care (LTC) settings. We sought to characterize depression symptom trajectories over six months among older residents, and to identify resident characteristics at baseline that predict symptom trajectory. METHODS: This study was a secondary analysis of data from a six-month prospective, observational, and multi-site study. Severity of depressive symptoms was assessed with the 15-item Geriatric Depression Scale (GDS) at baseline and with up to six monthly follow-up assessments. Participants were 130 residents with a Mini-Mental State Examination score of 15 or more at baseline and of at least two of the six monthly follow-up assessments. Individual resident GDS trajectories were grouped using hierarchical clustering. The baseline predictors of a more severe trajectory were identified using the Proportional Odds Model. RESULTS: Three clusters of depression symptom trajectory were found that described "lower," "intermediate," and "higher" levels of depressive symptoms over time (mean GDS scores for three clusters at baseline were 2.2, 4.9, and 9.0 respectively). The GDS scores in all groups were generally stable over time. Baseline predictors of a more severe trajectory were as follows: Initial GDS score of 7 or more, female sex, LTC residence for less than 12 months, and corrected visual impairment. CONCLUSIONS: The six-month course of depressive symptoms in LTC is generally stable. Most residents who experience a more severe symptom trajectory can be identified at baseline.
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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 | 0.001 |
| 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".