Screening for Depression in Patients in Long‐Term Care Facilities: A Randomized Controlled Trial of Physician Response
Bibliographic record
Abstract
OBJECTIVES: To determine the effect of a screening protocol using the Geriatric Depression Scale (GDS) on the frequency of primary care physicians' decisions to prescribe drug therapy or refer long-term care patients with possible depression to mental health care. DESIGN: Case-finding phase, followed by a randomized controlled trial of the effect of a physician-targeted intervention on antidepressant prescribing or referral to mental health services. SETTING: Twenty-two nonacademic long-term care facilities. PARTICIPANTS: One hundred three of 1,602 patients aged 65 and older who met criteria for cognitive function and untreated symptoms of depression. INTERVENTION: The 77 physicians of these patients were randomized as clusters into an early notification (experimental) or a delayed notification (control) group. MEASUREMENTS: Frequency of physician response (mental health consult or antidepressant therapy) at 4 and 8 weeks from notification, physician follow-up, and factors associated with physician response. RESULTS: Frequency of physician response in the early group (25%) was greater than in the delayed group (2%) (P <.005) 4 weeks from baseline. Physician response rate when the groups were combined was 36% (95% confidence interval (CI) = 26%-46%) 8 weeks from notification. Overall, there was evidence of physician action after letters of notification in 69% (95% CI = 60%-78%) of cases. Univariate logistic regression suggested that physicians' decisions were primarily associated with physician-related characteristics. CONCLUSIONS: Screening of long-term care patients for depression can increase the frequency of treatment or referral by primary care physicians.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".