Improving quality of care for persons with diabetes: an overview of systematic reviews - what does the evidence tell us?
Bibliographic record
Abstract
BACKGROUND: Ensuring high quality care for persons with diabetes remains a challenge for healthcare systems globally with consistent evidence of suboptimal care and outcomes. There is increasing interest in quality improvement strategies to improve diabetes management as reflected by a growing number of systematic reviews. These reviews are of varying quality and dispersed across many sources. In this paper, we present an overview of systematic reviews evaluating the impact of interventions to improve the quality of diabetes care. METHODS: We searched for systematic reviews evaluating the effectiveness of any intervention intended to improve intermediate patient outcomes and process of care measures for patients with any type of diabetes. Two reviewers independently screened search results, appraised each systematic review using AMSTAR and extracted data from high quality reviews (AMSTAR score ≥ 5). Within reviews, we used vote counting by direction of effect to report the number of studies favouring an intervention for each outcome. We produced summaries of results for each intervention category. RESULTS: We identified 125 reviews of varying methodological quality and summarised key findings from 50 high quality reviews. We categorised reviews by quality improvement intervention. Eight reviews were broad based (involving a variety of strategies). Other reviews considered: patient education and support (n = 21), telemedicine (n = 10), provider role changes (n = 7), and organisational changes (n = 4). Reviews reported intermediate patient outcomes (e.g. glycaemic control) (n = 49) and process of care outcomes (n = 9). There was evidence of considerable overlap of included studies between reviews. CONCLUSIONS: There is consistent evidence from high quality systematic reviews that patient education and support, provider role changes, and telemedicine are associated with improvements in glycaemic and vascular risk factor control in patients. There is less evidence about the impact of quality improvement interventions on other key process measures such as screening patients for diabetic complications. This paper provides decision makers with a comprehensive overview of evidence from high quality systematic reviews about the effects of quality improvement interventions on improving diabetes care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.048 | 0.201 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.015 |
| Bibliometrics | 0.022 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".