Multidisciplinary perinatal care for women with bleeding disorders
Bibliographic record
Abstract
Background: Women with bleeding disorders face increased bleeding risks during the perinatal period, making coordinated care essential to prevent and manage bleeding complications. Objectives: The study aimed to characterize the perinatal experiences of women with bleeding disorders and assess the impact of multidisciplinary care and care plans on patients and healthcare providers. Methods: Women aged >18 years with bleeding disorders who were provided a multidisciplinary care plan and underwent labor and delivery were recruited from St. Michael's Hospital Multidisciplinary Clinic for Women with Bleeding Disorders. Semistructured interviews were conducted until thematic saturation was reached, and transcripts were analyzed using descriptive qualitative analysis. Provider experiences were assessed using survey methods. Results: Fourteen participants were interviewed. Four primary themes were identified: 1) the impact of bleeding disorders on pregnancy, 2) challenges with the healthcare system, 3) St. Michael's Hospital Multidisciplinary Clinic for Women with Bleeding Disorders delivered patient-centered care, and 4) experience with the care plan. Eighteen healthcare providers completed the survey, with 89% supporting that the care plan was helpful and all agreeing that it met clinical needs. Conclusion: Our work demonstrates that although women with bleeding disorders face unique perinatal challenges, multidisciplinary care and individualized care plans improve confidence and empowerment and support advocacy-based, patient-centered 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".