Characteristics of men and women with diabetes: Observations during patients’ initial visit to a diabetes education centre
Bibliographic record
Abstract
OBJECTIVE: To determine whether men and women with type 2 diabetes have different psychosocial, behavioural, and clinical characteristics at the time of their first visit to a diabetes education centre. DESIGN: A questionnaire on psychosocial and behavioural characteristics was administered at participants’ first appointments. Clinical and disease-related data were collected from their medical records. Bivariate analyses (χ(2) test, t test, and Mann-Whitney test) were conducted to examine differences between men and women on the various characteristics. SETTING: Two diabetes education centres in the greater Toronto area in Ontario. PARTICIPANTS: A total of 275 men and women with type 2 diabetes. RESULTS: Women were more likely to have a family history of diabetes,previous diabetes education, and higher expectations of the benefits of self-management. Women reported higher levels of social support from their diabetes health care team than men did, and had more depressive symptoms, higher body mass, and higher levels of high-density lipoprotein cholesterol than men did. CONCLUSION: The results of this study provide evidence that diabetes prevention, care, and education need to be targeted to men and women differently. Primary care providers should encourage men to attend diabetes self-management education sessions and emphasize the benefits of self-care. Primary care providers should promote regular diabetes screening and primary prevention to women, particularly women with a family history of diabetes or a high body mass index; emphasize the importance of weight management for those with and without diabetes; and screen diabetic women for depressive symptoms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".