Bibliographic record
Abstract
OBJECTIVE To explore a once successful rural maternity care program and the variables surrounding its closure. DESIGN Analysis of archived logbook data, reports, and communications with medical staff. SETTING Bella Bella, a Heiltsuk First Nation community on British Columbia’s central coast. PARTICIPANTS Every patient delivering at the Bella Bella hospital since 1928. METHODS We extracted delivery rates, cesarean section rates, and local perinatal and maternal mortality rates from the hospital logbooks. In 2003, a consultant’s report reviewed the viability of surgical and maternity care services in Bella Bella; this was also reviewed. Finally, several personal communications with past and present medical staff added to an understanding of the issues that initially sustained and, in the end, closed the local maternity care program. MAIN FINDINGS Bella Bella had an intrapartum service with operative backup, and intervention and perinatal mortality rates were comparable to national data. There was only 1 maternal death in 80 years of intrapartum service. In the 1990s, with sparse cesarean section coverage, more mothers were obliged to travel to referral centres, until an eventual closure of the intrapartum care service in 2001. CONCLUSION Bella Bella provided safe and comprehensive maternity care until, in the context of an insufficient supply of family medicine generalists trained in anesthesia, surgery, and maternity care, the service closed.
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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 | 0.001 |
| Science and technology studies | 0.003 | 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) | 0.007 | 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".