Home or away? Factors affecting where women choose to give birth.
Bibliographic record
Abstract
OBJECTIVE: To investigate the factors that influence women to deliver their babies in small rural communities rather than in larger centres that have more comprehensive obstetric services, including cesarean section capability and epidural anesthesia. DESIGN: Self-administered survey. SETTING: Marathon, Ont, a rural community of 4500 in north western Ontario that offers low-risk obstetric services and has no local cesarean section capability. The closest referral centre, Thunder Bay, is 300 km away. PARTICIPANTS: Sixty-four women between 16 and 40 years old living in Marathon. MAIN OUTCOME MEASURES: The relative importance of personal and systemic factors and of beliefs that influence women to choose to give birth in Marathon rather than a larger centre. How well informed women are about local obstetric services. How likely women would be to choose to deliver in Marathon if they had low-risk pregnancies. RESULTS: Beliefs were more important than personal and systemic factors in influencing women's decisions. Respondents were moderately well informed about local obstetric services (mean proportion of correct responses was 66%). Most women with low-risk pregnancies would choose to deliver in Marathon (77.8%). CONCLUSION: For women in Marathon, beliefs are much more important than personal and systemic factors in influencing the decision to give birth in this small rural community.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".