‘I Am Not Going, So You're Doing It’: Management of Pregnant People With High BMI at Rural Hospitals in British Columbia: A Mixed Methods Analysis
Bibliographic record
Abstract
OBJECTIVE: To summarise administrative and interview data relevant to the care of pregnant people with high BMI in rural British Columbia. METHODS: In this mixed methods analysis, we report administrative health data linked to maternal postal codes, to determine variations in rates of local births across BMI groups (n = 3247) and to examine associations between BMI and adverse maternal-newborn outcomes (n = 2527). Qualitative data from 169 healthcare providers and administrators at rural hospitals in BC were analyzed to understand rural providers' experiences when caring for pregnant women with high BMI. Two clinical guidelines that were developed to improve care for women with obesity are presented, as examples of rural CQI initiatives. RESULTS: The proportion of local births decreased as BMI increased: 72.7% of those with normal BMI gave birth at rural hospitals compared to 34.9% with a BMI ≥ 40. For underweight, overweight, and obese women who gave birth at rural hospitals, the incidence of adverse perinatal outcomes was higher (IRR 1.85; 95% CI: 0.49-3.76, IRR = 1.19; 95% CI: 0.74-1.80; IRR = 1.81; 95% CI: 0.87-3.35), compared to those in the normal BMI range, but the associations were not significant. Healthcare providers also described challenges with maintaining quality and safety when caring for patients with high BMI in a low-volume rural setting and noted that a specialist 'cookie cutter' approach to managing and transferring people with high BMI was not practical in rural communities. DISCUSSION AND CONCLUSION: Strategies that improve the care of pregnant women with high BMI must take into account the social risks incurred by birthers and families who leave the community alongside the clinical risks of remaining, with attention also given to the impact of adverse outcomes on health care providers. These processes must be underscored by engagement from regional referral specialists to ensure local providers are clinically supported and that there are efficient transfer pathways to higher levels of care should this be needed.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".