The Long-Term Impact of Education on Diabetes for Older People: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Although enthusiasm is growing for diabetic education programs for older people, data regarding their effectiveness and their long-term impact on self-management were neglected. PURPOSE: To systematically review diabetes mellitus education that has long-term effects on the self-management of older diabetic people. DATA SOURCES: The authors searched multiple sources dated through September 2012, including the Cochrane Library, MEDLINE, PsycINFO, Nursing and Allied Health databases, and the bibliographies of 50 previous reviews. METHODS AND DATA EXTRACTION: Electronic databases were searched for controlled studies in English, published from 1987 to 2012, assessing the effects of long-term education for older people. Reviewers extracted study data using a structured abstraction form. Aggregated information about the effects of long-term education programs on older people with diabetes was used for making adjustments in the review. RESULTS: The pooled estimate of the long-term effects of education was a 0.5 percentage point reduction (95% confidence interval), modest but significant improvement. The evidence also supports that long-term education is beneficial for improving diabetic patient self-care management in terms of glycemic control.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".