Patient factors associated with attrition from a self‐management education programme
Bibliographic record
Abstract
OBJECTIVE: To examine utilization patterns of diabetes self-management training (DSME) and identify patient factors associated with attrition from these services at an ambulatory diabetes education centre (DEC). METHODS: A retrospective medical chart review of first time visits (536) to the centre between 1 August 2000 and 31 July 2001 was conducted for patients with type 2 diabetes. Descriptive analyses were conducted to examine utilization patterns over a 1-year period. Multivariable logistic regression was used to identify patient factors associated with attrition from DSME and non-use of group education among new patients. RESULTS: Almost 50% of new patients withdrew prematurely from recommended DSME services over the 1-year period, and only 24.8% attended group education. Patient variables such as being older than 65 years of age, primarily speaking English, or working full or part-time were associated with attrition from DSME and non-use of group education when compared with middle aged, non-English-speaking, and non-working patients. CONCLUSIONS: High DSME attrition rates indicate that retention needs to become a focus of programme policy, planning and evaluation to improve programme effectiveness. DSME tailored to the cultural and linguistic characteristics of the community, and convenient and accessible to working and older patients will potentially increase retention in and accessibility to these services.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".