Does a 2.5‐year self‐management education and support intervention change patterns of healthcare use in African‐American adults with Type 2 diabetes?
Bibliographic record
Abstract
AIMS: To investigate the impact of a 2.5-year diabetes self-management education and support intervention on healthcare use and to examine factors associated with patterns of healthcare use. METHODS: We recruited 60 African-American adults with type 2 diabetes who completed a 2.5-year empowerment-based diabetes self-management education and support intervention. Primary healthcare use outcomes included acute care visits, non-acute care visits and days lost to disability. Acute care was a composite score calculated from the frequency of urgent care visits, emergency department visits and hospitalizations. Non-acute care measured the frequency of scheduled outpatient visits. To examine change in patterns of healthcare use, we compared the frequency of healthcare visits over the 6-month period preceding the intervention with that in the last 6 months of the intervention. RESULTS: No significant changes in patterns of healthcare use were found for acute care, non-acute care or days lost to disability. Multiple regression models found higher levels of depression (P = 0.001) to be associated with a greater number of non-acute healthcare visits, and found longer duration of diabetes (P = 0.019) and lower levels of diastolic blood pressure (P = 0.025) to be associated with fewer days lost to disability. CONCLUSIONS: Participation in a long-term diabetes self-management education and support intervention had no impact on healthcare use in our sample of African-American subjects.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".