Bibliographic record
Abstract
Midwifery in Ontario, Canada is expanding beyond its original model of care, designed more than thirty years ago to challenge the mainstream medical model of pregnancy and birth care. Midwives are forging new interprofessional collaborations, proposing expanded practice models to offer a greater range of clinical services and reach underserved communities and clients, and trying on new flexible funding arrangements to sustain their work. Such “experiments in care” are part of a long history within midwifery of advocating for change within the health care system. Recognising limitations and constraints in their original model of care midwives are attending to new advocacy projects of diversity, equity and Indigenous wellbeing. They have found some willing partners in the health professions – public health practitioners, obstetrician-gynaecologist (OBGYN), family physicians, and addiction specialists – as well as support from the provincial bodies that regulate and fund the profession. This chapter reports on research conducted during the COVID-19 pandemic and highlights three themes that describe how midwives are evolving their work: expanding the midwifery clinical scope of practice; forging interprofessional collaborations; and producing evidence of new ways of delivering care. Notably “experiments in care” are battles of a different sort; less about jurisdiction over birth care and more about the pursuit of equitable and high-quality health care.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.018 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".