Disparities in attendance at diabetes self-management education programs after diagnosis in Ontario, Canada: a cohort study
Bibliographic record
Abstract
BACKGROUND: Patients newly-diagnosed with diabetes require self-management education to help them understand and manage the disease. The goals of the study were to determine the frequency of diabetes self-management education program utilization by newly-diagnosed patients, and to evaluate whether there were any demographic or clinical disparities in utilization. METHODS: Using population-level health care data, all 46,553 adults who were diagnosed with any type of non-gestational diabetes in Ontario, Canada between January and June 2006 were identified. They were linked with a diabetes self-management education program registry to identify those who attended within 6 months of diagnosis. The demographic and clinical characteristics of attendees and non-attendees were compared. RESULTS: A total of 9,568 (20.6%) patients attended a diabetes self-management education program within 6 months of diagnosis. Younger age, increasing socioeconomic status, and the absence of mental health conditions or other medical comorbidity were associated with attendance. Patients living in rural areas, where access to physicians may be limited, were markedly more likely to attend. Recent immigrants were 40% less likely to attend self-management education programs than longer-term immigrants or nonimmigrants. CONCLUSION: Only one in five newly-diagnosed diabetes patients attended a diabetes self-management education program. Demographic and clinical disparities in utilization persisted despite a publicly-funded health care system where patients could access these services without direct charges. Primary care providers and education programs must ensure that more newly-diagnosed diabetes patients receive self-management education, particularly those who are older, poorer, sicker, or recent immigrants.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".