Feasibility of establishing a Canadian Obstetric Survey System (CanOSS) for severe maternal morbidity: a study protocol
Bibliographic record
Abstract
INTRODUCTION: Severe maternal morbidity (SMM)-an unexpected pregnancy-associated maternal outcome resulting in severe illness, prolonged hospitalisation or long-term disability-is recognised by many, as the preferred indicator of the quality of maternity care, especially in high-income countries. Obtaining comprehensive details on events and circumstances leading to SMM, obtained through maternity units, could complement data from large epidemiological studies and enable targeted interventions to improve maternal health. The aim of this study is to assess the feasibility of gathering such data from maternity units across Canadian provinces and territories, with the goal of establishing a national obstetric survey system for SMM in Canada. METHODS AND ANALYSIS: We propose a sequential explanatory mixed-methods study. We will first distribute a cross-sectional survey to leads of all maternity units across Canada to gather information on (1) Whether the unit has a system for reviewing SMM and the nature and format of this system, (2) Willingness to share anonymised data on SMM by direct entry using a web-based platform and (3) Respondents' perception on the definition and leading causes of SMM at a local level. This will be followed by semistructured interviews with respondent groups defined a priori, to identify barriers and facilitators for data sharing. We will perform an integrated analysis to determine feasibility outcomes, a narrative description of barriers and facilitators for data-sharing and resource implications for data acquisition on an annual basis, and variations in top-5 causes of SMM. ETHICS AND DISSEMINATION: The study has been approved by the Mount Sinai and Hamilton Integrated Research Ethics Boards. The study findings will be presented at annual scientific meetings of the Society of Obstetricians and Gynaecologists of Canada, North American Society of Obstetric Medicine, and International Network of Obstetric Survey Systems and published in an open-access peer-reviewed Obstetrics and Gynaecology or General Internal Medicine journal.
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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 | 0.185 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".