Rural maternity care services under stress: the experiences of providers.
Bibliographic record
Abstract
INTRODUCTION: Between 2000 and 2004, 17 small rural maternity care services in British Columbia (BC) closed or were placed under moratoria. This paper explores the experiences of care providers in 4 rural BC communities that have lost or are at risk of losing their local maternity services. METHODS: We conducted qualitative, semistructured interviews and focus groups with 27 health care providers (doctors and nurses) and 3 administrators. The analysis used modified grounded theory. We chose 4 rural communities to include a diversity of characteristics, including community size, geography, distance to the nearest hospital capable of performing cesarean section, and cultural and ethnic subpopulations. RESULTS: Care providers identified significant stressors related to the provision of maternity care services, including the development and maintenance of competency in the context of decreasing birth volume, the safety of local maternity care without cesarean section and the desire to balance women's needs with the realities of rural practice. CONCLUSIONS: Maternity care providers in small rural communities are experiencing stress due in part to the absence of evidence-based policy and planning for rural maternity care services. This stress may contribute to challenges in the retention of rural maternity care providers, thus risking the future of small rural maternity services.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".