A Randomized Controlled Trial to Increase Cancer Screening and Reduce Depression among Low-Income Women
Bibliographic record
Abstract
Abstract Background Women from low-income, racial/ethnic minority backgrounds receive fewer cancer screenings than other women and have higher rates of depression, which can interfere with cancer screening participation. Objective Assess the comparative effectiveness of two interventions to improve breast, cervical, and colorectal cancer screening and reduce depression among underserved women with depressive symptoms. Design Randomized comparative effectiveness trial Setting Six Federally Qualified Health Centers Participants N=757, 50-64 years with depression symptoms and overdue for cancer screening. Interventions Participants were randomized to collaborative depression care plus cancer screening intervention ( Collaborative Care Intervention , CCI) or cancer screening intervention alone ( Prevention Care Management , PCM). Both of these evidence-based, telephone interventions were delivered in English or Spanish, for up to 12 months, by care managers. Measurements Electronic Health Records provided cancer screening data (primary outcome). PHQ-9 measured depression at study entry (T0), 6- and 12-months post-baseline (T1 and T2, respectively; secondary outcome). Results Analyses revealed statistically significant increases in up-to-date status for all cancer screenings; depression improved in both intervention groups. There were no statistically significant differences between the two interventions in improving cancer screening rates or reducing depression. Limitations Depressive symptom improvement may be explained by typical symptom remission. The study duration (12-months) was insufficient to evaluate intervention effects on long-term cancer screening behavior and depression. This study was not powered for site-level analysis. Conclusions CCI and PCM both improved breast, cervical, and colorectal cancer screening and depression in clinical settings in underserved communities, however neither intervention showed an advantage in outcomes. Decisions about which approach to implement may depend on the nature of the practice and alignment of the interventions with other ongoing priorities and resources. Funding source This study was funded by Patient Centered Outcomes Research Institute (PCORI) (IH-12-11-4522) with additional infrastructure support from Agency for Healthcare Research and Quality (AHRQ) (5P30-HS-021667).
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".