Gestational Diabetes History May Signal Deprivation in Women with Type 2 Diabetes
Bibliographic record
Abstract
BACKGROUND: There is a higher prevalence of type 2 diabetes in lower income groups, particularly in women. Gestational diabetes (diabetes during pregnancy) has also been associated with lower income levels. What has not been studied is whether a past history of gestational diabetes retains an inverse association with income among women with type 2 diabetes. METHODS: Among women with type 2 diabetes, we assessed for an association between history of gestational diabetes and lower income/lower educational attainment (multiple waves of Canadian Community Health Survey [CCHS]) through logistic regression models adjusted for age, body mass index (BMI), immigrant and marital status, smoking history, and physical activity. RESULTS: Compared to women in the highest income group, a gestational diabetes history was 71% more likely in the lower middle income group (odds ratio [OR] 1.71, 95% confidence interval [CI] 1.06-2.74) and nearly two times more likely in the lowest income group (OR 1.94, 95% CI 1.15-3.27). Associations with education were inconclusive. Compared to married women, single women (divorced/separated/never married) were nearly two times more likely to have a gestational diabetes history (OR 1.71, 95% CI 1.17-2.49). CONCLUSIONS: These findings indicate that women with diabetes and past history of gestational diabetes constitute a particularly deprived group. A gestational diabetes history in women with type 2 diabetes may signal a need to assess and address material resources and social support as part of the diabetes management plan.
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 | 0.001 | 0.007 |
| 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 | 0.001 |
| 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".