The Integration of Ontario Birth Centers into Existing Maternal‐Newborn Services: Health Care Provider Experiences
Bibliographic record
Abstract
INTRODUCTION: In 2014, 2 freestanding, midwifery-led birth centers opened in Ontario, Canada. The purpose of this study was to qualitatively investigate the integration of the birth centers into the local, preexisting intrapartum systems from the perspective of health care providers and managerial staff. METHODS: Focus groups or interviews were conducted with health care providers (paramedics, midwives, nurses, physicians) and managerial staff who had experienced urgent and/or nonurgent maternal or newborn transports from a birth center to one of 4 hospitals in Ottawa or Toronto. A descriptive qualitative approach to data analysis was undertaken. RESULTS: Twenty-four health care providers and managerial staff participated in a focus group or interview. Participants described positive experiences transporting women and/or newborns from the birth centers to hospitals; these positive experiences were attributed to the collaborative planning, training, and communication that occurred prior to opening the birth centers. The degree of integration was dependent on hospital-specific characteristics such as history, culture, and the presence or absence of midwifery privileging. Participants described the need for only minor improvements to administrative processes as well as the challenge of keeping large numbers of staff updated with respect to urgent transport policies. Planning and opening of the birth centers was seen as a driving force in further integrating midwifery care and improving interprofessional practice. DISCUSSION: The collaborative approach for the planning and implementation of the birth centers was a key factor in the successful integration into the existing maternal-newborn system and contributed to improving integrated professional practice among midwives, paramedics, nurses, and physicians. This approach may be used as a template for the integration of other new independent health care facilities and programs into the existing health care system.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".