The Feasibility of Establishing a Canadian Obstetric Survey System (CanOSS) for Severe Maternal Morbidity: Interim Results [ID: 1377349]
Bibliographic record
Abstract
INTRODUCTION: Obtaining granular data on events and circumstances leading to severe maternal morbidity (SMM) could complement large epidemiologic studies and enable targeted interventions to improve maternal health. This study aims to assess the feasibility of gathering such data from maternity units across Canada through a Canadian Obstetric Survey System (CanOSS). METHODS: The study is a sequential explanatory mixed-methods study, the first step of which is a nationwide survey of maternity unit leads. Semistructured qualitative interviews are being conducted with unit leads that expressed interest in response to the survey. Responses are reported using proportions and percentages. Interviews are being thematically analyzed. This study was approved by the Hamilton Integrated Research Ethics Board (HiREB) #14002. RESULTS: As of October 18, 2022, we have identified 306 maternity units nationally, and sent 218 surveys, with 59 maternity units across 8 provinces completing. Among these, 34 (83%) report having a system in place for reviewing SMM, conducted on an as-needed basis in 61% of units, and most commonly involving a multidisciplinary panel of experts with representation from nursing (85%), maternity unit leadership (79%), and hospital management (74%). A written report is prepared following 76% of meetings. Findings are shared with health care professionals involved in the event and formulated into recommendations in 79% and 82% of units, respectively. Importantly, 78% of respondents would be willing to contribute anonymized data on SMM within a centralized reporting system. Interviews have taken place with 14 unit leads. Interviewed participants unanimously agree that an obstetric survey system is needed. Concerns raised include privacy, resources, funding, and having a clear definition of SMM. CONCLUSION: This feasibility study will facilitate future planning, clarify barriers to be addressed, and resource implications for gathering data on SMM at a national level, and lay the foundations for a future CanOSS.
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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 | 0.031 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".