Integrated Community Collaborative Care for Seniors with Depression/Anxiety and any Physical Illness
Bibliographic record
Abstract
BACKGROUND: We report on the feasibility and effectiveness of an integrated community collaborative care model in improving the health of seniors with depression/anxiety symptoms and chronic physical illness. METHODS: This community collaborative care model integrates geriatric medicine and geriatric psychiatry with care managers (CM) providing holistic initial and follow-up assessments, who use standardized rating scales to monitor treatment and provide psychotherapy (ENGAGE). The CM presents cases in a structured case review to a geriatrician and geriatric psychiatrist. Recommendations are communicated by the CM to the patient's primary care provider. RESULTS: 187 patients were evaluated. The average age was 80 years old. Two-thirds were experiencing moderate-to-severe depression upon entry and this proportion decreased significantly to one-third at completion. Qualitative interviews with patients, family caregivers, team members, and referring physicians indicated that the program was well-received. Patients had on average six visits with the CM without the need to have a face-to-face meeting with a specialist. CONCLUSION: The evaluation shows that the program is feasible and effective as it was well received by patients and patient outcomes improved. Implementation in fee-for-service publicly funded health-care environments may be limited by the need for dedicated funding.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".