Predicting Diagnosed Depression and Anti-depressant Treatment in Institutionalized Older Adults by Symptom Profiles: A Closer Look at Anhedonia and Dysphoria
Bibliographic record
Abstract
The purpose of this study was to examine the relationships of diagnosis and treatment of depression with anhedonic and dysphoric symptom presentation, using the Minimum Data Set 2.0. Participants were from two sectors of longterm care: 70 nursing home residents and 92 residents in a Veterans' Care Service. The samples differed in their sex distribution and in cognition. A series of logistic regressions that controlled for demographics, type of facility, and cognition showed that dysphoric symptoms predicted diagnosed depression, whereas anhedonic symptoms predicted anti-depressant medication use without a concomitant diagnosis. The findings are consistent with a hypothesis that, in long-term care settings, anhedonic symptoms contribute less to a diagnosis of depression than do dysphoric symptoms. However, findings that anhedonic symptoms relate to treatment have implications for care-planning protocols.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".