Unexpected termination of pregnancy for fetal anomaly: a patient resource manual
Bibliographic record
Abstract
Background: Registered nurses (RNs) working in a Women’s Health Inpatient Unit (WHIU) \nencounter approximately 30 miscarriages and stillbirths per year, including the need for \nunexpected termination of pregnancies due to fetal anomaly. Many patients lack education or \npreparation for labour stimulation, leading to emotional distress. A patient resource manual \n(PRM) can help educate and assist to alleviate some distress for these patients and families. \nPurpose: To develop a resource for patients and families undergoing a planned termination of \npregnancy for fetal anomaly. Methods: I completed a literature review, an environmental scan of \nlocal and regional facilities that offer planned pregnancy terminations, and consultations with \nlocal key stakeholders. Results: Findings from the literature review, consultations, and \nenvironment scan supported the need for an educational resource about the termination of \npregnancy for patients and families. The most appropriate mode of delivery is a PRM in \nprintable booklet form, and the content should include expectations with admission to the \ninpatient unit, the step-for-step labour process with vaginal delivery, and expectations for \nmedication administration. Conclusion: The creation of the PRM, a patient guide to treatment \nfor termination of pregnancy, is expected to enhance the knowledge of patients and families \nbefore beginning the termination process. It is further anticipated that this PRM will ease patient \nsuffering and improve coping during the termination and subsequent bereavement process, \nleading to fewer adverse health outcomes for patients and families.
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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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".