Moderate mental illness as a predictor of chronic disease prevention and screening
Bibliographic record
Abstract
BACKGROUND: Primary care plays a pivotal role in the provision of both mental health care and chronic disease prevention and screening (CDPS). Unfortunately, moderate mental illness (MMI) is associated with poorer general health outcomes. Part of this burden of illness may be due to reduced CDPS uptake. The Building on the Existing Tools to Improve Chronic Disease Prevention in Primary Care (BETTER) trial recruited 777 participants ages 40-65 from 32 family practice panels, of whom 135 (18.2%) had elevated GAD scores, 118 (16.4%) had elevated PHQ scores, and 264 (34.0%) had electronic medical record (EMR)-documented MMI. We hypothesized that patients with screen-positive or chart-documented MMI are 1) eligible for more CDPS actions, and 2) able to complete a lower proportion of CDPS actions than patients unaffected by MMI. METHODS: This study was a secondary analysis of data from the BETTER trial. Participants were stratified by both EMR-documented MMI and screen-positive evidence of MMI (using the General Anxiety Disorders (GAD-7) and Patient Health Questionnaire (PHQ-9) instruments for anxiety and depression screening, respectively). The primary outcome was the proportion of CDPS actions for which the patient was eligible completed at follow-up, using a composite index. RESULTS: After adjusting for age, gender, and social support, patients with evidence of MMI had a lower composite index than patients without evidence of MMI (p < 0.05). The lower composite index is primarily due to higher eligibility for CDPS at baseline; ability to complete CDPS was not statistically different. CONCLUSIONS: Patients affected by MMI are eligible for more CDPS actions than their unaffected counterparts. Although they are able to complete a similar number of CDPS actions, they are not able to eliminate their baseline CDPS gap. Primary care teams need to be aware of this increased CDPS eligibility for patients with MMI and ensure best practices in CDPS supports are available to this patient population. Further study is needed to determine the ideal suite of targeted supports.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".