Predictors of a new depression diagnosis among older adults admitted to complex continuing care: implications for the depression rating scale (DRS)
Bibliographic record
Abstract
BACKGROUND: depression is a major disabling condition among older adults, where it may be under-diagnosed for a number of reasons, including a different presentation for younger people with depression. The Minimum Data Set 2.0 (MDS 2.0) assessment system provides a measurement scale for depression, the Depression Rating Scale (DRS), in addition to other items that may represent depressive phenomenology. OBJECTIVE: the ability of the DRS to predict the presence of new depression diagnoses at follow-up, among hospitalised older adults admitted without depression, is examined. METHODS: the study sample consists of all persons aged 65 years or more admitted between 1996 and 2003 to a complex continuing care (CCC) bed in Ontario without a recorded depression diagnosis. The sample was restricted to those who remained in hospital for about 3 months (n = 7,818) in order to obtain follow-up assessment information. Logistic regression was used to explore the relationship between admission characteristics (i.e. DRS scale items, other MDS 2.0 items related to DSM-IV criteria for depression) and receipt of a depression diagnosis on the follow-up assessment. RESULTS: a new depression diagnosis at follow-up was present in 7.5% of the individuals. The multivariate model predicting depression diagnosis included only the DRS scale, sadness over past roles, and withdrawal from activities. CONCLUSIONS: the DRS score at admission was predictive of receiving a depression diagnosis on a follow-up assessment among older adults admitted to the CCC. Further, the predictive ability of the DRS is only modestly improved by the addition of other items related to DSM-IV criteria.
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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".