Mental Health Referrals Among Medicare Advantage Enrollees Receiving Home-Based Annual Wellness Visits in Puerto Rico During the COVID-19 Pandemic
Bibliographic record
Abstract
Annual Wellness Visits (AWV) promote preventive care for older adults, yet uptake remains low. To increase AWVs, a Medicare Advantage (MA) plan in Puerto Rico contracted a medical group to provide home-based AWVs during the last quarter of 2020. Using data from 464 visits, we conducted descriptive and multivariable analysis to profile patient characteristics and identify predictors of mental health referrals. We found that 87% of patients had multiple chronic conditions, 75% were taking more than 5 medications, and the odds of a mental health referral were higher for those who also had a nutrition-related condition (AOR = 5.05, CI 95 : 1.76–11.88), diabetes (AOR = 3.34, CI 95 : 1.18–7.58), or an additional reported uncontrolled health issue (AOR = 28.18, CI 95 : 8.96–70.59). This strategy helped one MA plan reach high-need patients, but coordination of follow-up care is needed to ensure patients receive recommended services.
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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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".