Comparison of Community Health Worker–Led Diabetes Medication Decision-Making Support for Low-Income Latino and African American Adults With Diabetes Using E-Health Tools Versus Print Materials
Bibliographic record
Abstract
BACKGROUND: Health care centers serving low-income communities have scarce resources to support medication decision making among patients with poorly controlled diabetes. OBJECTIVE: To compare outcomes between community health worker use of a tailored, interactive, Web-based, tablet computer-delivered tool (iDecide) and use of print educational materials. DESIGN: Randomized, 2-group trial conducted from 2011 to 2013 (ClinicalTrials.gov: NCT01427660). SETTING: Community health center in Detroit, Michigan, serving a Latino and African American low-income population. PARTICIPANTS: 188 adults with a hemoglobin A1c value greater than 7.5% (55%) or those who reported questions, concerns, or difficulty taking diabetes medications. INTERVENTION: Participants were randomly assigned to receive a 1- to 2-hour session with a community health worker who used iDecide or printed educational materials and 2 follow-up calls. MEASUREMENTS: Primary outcomes were changes in knowledge about antihyperglycemic medications, patient-reported medication decisional conflict, and satisfaction with antihyperglycemic medication information. Also examined were changes in diabetes distress, self-efficacy, medication adherence, and hemoglobin A1c values. RESULTS: Ninety-four percent of participants completed 3-month follow-up. Both groups improved across most measures. iDecide participants reported greater improvements in satisfaction with medication information (helpfulness, P = 0.007; clarity, P = 0.03) and in diabetes distress compared with the print materials group (P < 0.001). The other outcomes did not differ between the groups. LIMITATIONS: The study was conducted at 1 health center during a short period. The community health workers were experienced in behavioral counseling, thereby possibly mitigating the need for additional support tools. CONCLUSION: Most outcomes were similarly improved among participants receiving both types of decision-making support for diabetes medication. Longer-term evaluations are necessary to determine whether the greater improvements in satisfaction with medication information and diabetes distress achieved in the iDecide group at 3 months translate into better longer-term diabetes outcomes. PRIMARY FUNDING SOURCE: Agency for Healthcare Research and Quality and National Institute of Diabetes and Digestive and Kidney Diseases.
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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 | 0.003 | 0.002 |
| 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 | 0.000 |
| 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".