The Process of Informed Decision-Making About Prenatal Screening: Patient Education Materials, Policy, and Pregnant Women's Perspectives
Bibliographic record
Abstract
This thesis examines the process of informed decision-making for low-risk women in their first pregnancies as they consider whether or not to participate in non-invasive, non-diagnostic prenatal screening.\nUsing a social constructionist approach with a theoretical lens of feminist bioethics, data from three sources were used to consider different aspects of the process of informed decision-making about prenatal screening. Data obtained from patient education materials, Society of Obstetricians and Gynecologists of Canada policy documents, and interviews with pregnant women were examined using the principles of constructivist grounded theory.\nFindings are presented in the form of four integrated articles, addressing the issues of: I) the potential for metaphors and figurative language in prenatal patient education materials to be implicitly or explicitly directive; II) different counseling strategies and decision-making models for health care providers to consider when striving to offer prenatal screening in a way which supports autonomous informed choices; III) the type and content of information that women have identified as necessary to consider themselves informed when preparing to make a decision about participation in prenatal screening; IV) a proposed model for the ways in which information from different sources is sought, received, and interpreted by women engaged in the process of making a decision about prenatal screening.\nInformed decision-making about prenatal screening is a complex issue, requiring significant efforts on the part of the health care professional offering screening, and necessitating careful consideration by the woman making the choice. Given an understanding of the complex social context within which these screening tests are offered and chosen, the process of informed decision-making may be more challenging than suggested by current policy and public education documents.
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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 | 0.054 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.027 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".