Navigating early pregnancy loss within Ontario's healthcare system: A qualitative exploratory study of the experiences of midwifery clients and midwives
Bibliographic record
Abstract
Background: Miscarriage occurs in approximately 25% of all pregnancies. About 80% of all pregnancy losses occur in the first trimester. Patient experiences of seeking and receiving healthcare for early pregnancy loss can have long-term implications on their well-being. While individuals often present to emergency departments (ED) with early pregnancy loss symptoms, evidence suggests patient needs are not being met within this setting. There is a dearth of research on women’s experiences utilizing the midwifery care as an option for early pregnancy loss. \nResearch Questions: This exploratory qualitative study examines two primary research questions: (1) What are the experiences of Ontario midwifery clients accessing and receiving healthcare in cases of early pregnancy loss (EPL); and (2) What are the experiences of midwives in providing early pregnancy loss care for their clients? The overall objective of this study is to understand how the healthcare-related experiences can be improved in cases of early pregnancy loss. \nMethods: Semi-structured qualitative interviews were conducted with midwifery clients (n=14) and midwives (n=10). Two analytic approaches were taken for the analysis of participant interview data: healthcare journey mapping and thematic network techniques. \nFindings: Both the healthcare trajectories and experiences of clients accessing and receiving midwifery care for early pregnancy loss varied considerably. Four main themes were identified as the aspects of midwifery care that made the biggest differences on clients’ experiences of receiving care for early pregnancy loss: (1) Accessing care for early pregnancy loss, (2) Continuity and following-through, (3) Compassionate and supportive care, and (4) Knowledge, information and choice. Overall, the findings suggest clients benefit from compassionate, individualized support during their early pregnancy loss. Midwives’ experiences constraints related to their workload, clinic culture, local resources available, and compensation model that impacted their ability to respond to clients’ needs and expectations. \nConclusion: Interventions to improve client care should look beyond client-provider interactions and consider ways to improve midwives’ experiences and their ability to meet their client needs. Furthermore, to improve women’s experiences, a more coordinated, patient-centered response at a systems level is needed. As this is the first study to examine the midwifery model of care for early pregnancy loss, findings from this study contribute to recommendations for practice, policy, and research.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 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".