Reasons why patients referred to diabetes education programmes choose not to attend: a systematic review
Bibliographic record
Abstract
AIMS: To identify the reasons why those offered a place on diabetes education programmes declined the opportunity. BACKGROUND: It is well established that diabetes education is critical to optimum diabetes care; it improves metabolic control, prevents complications, improves quality of life and empowers people to make informed choices to manage their condition. Despite the significant clinical and personal rewards offered by diabetes education, programmes are underused, with a significant proportion of patients choosing not to attend. METHODS: A systematic search of the following databases was conducted for the period from 2005-2015: Medline; EMBASE; Scopus; CINAHL; and PsycINFO. Studies that met the inclusion criteria focusing on patient-reported reasons for non-attendance at structured diabetes education were selected. RESULTS: A total of 12 studies spanning quantitative and qualitative methodologies were included. The selected studies were published in Europe, USA, Pakistan, Canada and India, with a total sample size of 2260 people. Two broad categories of non-attender were identified: 1) those who could not attend for logistical, medical or financial reasons (e.g. timing, costs or existing comorbidities) and 2) those who would not attend because they perceived no benefit from doing so, felt they had sufficient knowledge already or had emotional and cultural reasons (e.g. no perceived problem, denial or negative feelings towards education). Diabetes education was declined for many reasons, and the range of expressed reasons was more diverse and complex than anticipated. CONCLUSION: New and innovative methods of delivering diabetes education are required which address the needs of people with diabetes whilst maintaining quality and efficiency.
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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 | 0.011 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".