Canadian midwives’ perspectives on the clinical impacts of point of care ultrasound in obstetrical care: A concurrent mixed-methods study
Bibliographic record
Abstract
Introduction: Point of Care Ultrasound (POCUS) is used globally in obstetrics to conduct real time bedside ultrasound scans to answer a clinical question, and it may be conducted by a non-sonography healthcare practitioner. The College of Midwives of Ontario expanded the scope of practice in 2018 to allow registered midwives to perform POCUS during clinical assessments. In response, a POCUS training curriculum for practicing midwives was developed. This paper reports on the perceptions of learners about the impact of this training on their clinical practice. Methods: We conducted a mixed-methods study to understand learner perceptions. Data collection included surveys at four time points over a year, and semi-structured interviews. Quantitative data were analyzed through descriptive statistics, and qualitative analyses used a constructivist approach to grounded theory. Results: The frequency of POCUS use within antenatal care increased among learners, with common applications including assessment of fetal presentation and confirmation of viability. POCUS was seen to holistically aid practitioners care by providing additional skills and knowledge to improve care quality and access to care, particularly for remote areas where ultrasounds are not easily available. However, participants articulated a need for clearer regulatory guidelines outlining how this technology should be applied in midwifery. Equipment purchasing and maintaining costs were a barrier for many midwives. Conclusions: Participants who had access to a device are continuing to use sonography within their clinics to provide comprehensive midwifery care informed by real-time ultrasound assessments. POCUS scans were seen to offer many benefits to improve patient 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 | 0.030 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".