Obstetrical care closer to home: team-based high risk care in Canada's Arctic
Bibliographic record
Abstract
The Qikiqtani Region of Nunavut is served by a regional hospital with over 400 births annually. In Nunavut, age-specific fertility rates are over 8 times the national average, with increased pre-eclampsia, postpartum hemorrhage, and preterm birth. For Inuit women, giving birth closer to home, instead of in a city over 2000 km away, is of paramount importance. In a region with no obstetrician, this has posed challenges for family physicians. To provide safe, culturally-relevant care, a multi-faceted approach was developed. Care is delivered by a core team of full-time family physicians. A comprehensive orientation guide was created, with protocols to standardize and clarify the scope of the team's capabilities. Developed from existing guidelines, use of the MoreOB program, and in consultation with Maternal Fetal Medicine, these were modified to the local context. A consensus process is used to update protocols at an annual physician retreat. Diagnostic tools (ex. fetal fibronectin) are available in all communities. Telehealth is used for weekly multidisciplinary rounds, where community nurses join in reviewing all high-risk pregnancies and women at term. Mandatory consultation with another family physician exists for decisions regarding induction or caesarian section. Physicians are increasingly able to manage complex cases and caesarian section rates are below the National average (<10%). Need for out-of-region referrals has decreased, due to early initiation of preventative therapies for preterm birth or preeclampsia. Challenges include increasing physician numbers, integration of midwifery in a setting with no obstetrician, and inability to expand telehealth due to internet bandwidth limitations.This abstract was presented at the Innovative Solutions in Remote Healthcare - 'Rethinking Remote' conference, 23-24 May 2016, Inverness, Scotland.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.001 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".