Pregnancy and development of diabetes in First Nations and non‐First Nations women in Alberta, Canada
Bibliographic record
Abstract
AIM: To conduct a retrospective population-based study to examine the risk of developing diabetes after delivery in First Nations and non-First Nations women in Alberta. METHODS: Delivery records (1999-2014) were linked to provincial administrative data, which allowed for a maximum follow-up of 16 years after delivery. Prevalence of pregnancy risk factors were compared by First Nations status. Hazard ratios for diabetes after delivery by First Nations status, high pre-pregnancy body weight (≥91 kg) and gestational diabetes status were estimated using the Cox proportional hazards model. RESULTS: Age-adjusted prevalence of gestational diabetes (7.9% vs 4.6%; P<0.0001), high pre-pregnancy body weight (18.8% vs 10.2%; P<0.0001) and diabetes after delivery (3.9% vs 1.1%; P<0.0001) were higher in First Nations women than in non-First Nations women. Development of diabetes after delivery was higher with First Nations status (hazard ratio 3.0, 95% CI 2.6-3.4), high pre-pregnancy body weight (hazard ratio 3.6, 95% CI 3.3-4.0) and gestational diabetes status (hazard ratio 19.2, 95% CI 17.9-20.6). The highest risk was within First Nations women with high pre-pregnancy body weight and gestational diabetes (hazard ratio 54.8, 95% CI 45.2-66.5) compared to women without these three risk factors. Reduced prenatal visits per pregnancy (8.4 vs 10.7; P<0.0001) and delayed first prenatal visit (time to delivery 23.7 vs 26.7 weeks; P<0.0001) were observed in First Nations women compared to non-First Nations women. CONCLUSION: First Nations women are at greater risk of developing diabetes after pregnancy, with gestational diabetes being the strongest predictor. Strategies that target the specific needs of First Nations women before, during and after pregnancy are required.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".