Exploring informed choice in the context of prenatal testing: findings from a qualitative study
Bibliographic record
Abstract
PURPOSE: This study explored whether and how a sample of women made informed choices about prenatal testing for foetal anomalies; its aim was to provide insights for future health policy and service provision. METHODS: We conducted semi-structured interviews with 38 mothers in Ottawa, Ontario, all of whom had been offered prenatal tests in at least one pregnancy. Using the Multi-dimensional Measure of Informed Choice as a general guide to analysis, we explored themes relevant to informed choice, including values and knowledge, and interactions with health professionals. RESULTS: Many, but not all, participants seemed to have made informed decisions about prenatal testing. Values and knowledge were interrelated and important components of informed choice, but the way they were discussed differed from the way they have been presented in scientific literature. In particular, 'values' related to expressions of women's moral views or ideas about 'how life should be lived' and 'knowledge' related to the ways in which women prioritized and interpreted factual information, through their own and others' experiences and in 'thinking through' the personal implications of testing. While some women described non-directive discussions with health professionals, others perceived testing as routine or felt pressured to accept it. CONCLUSIONS: Our findings suggest a need for maternity care providers to be vigilant in promoting active decision making about prenatal testing, particularly around the consideration of personal implications. Further development of measures of informed choice may be necessary to fully evaluate decision support tools and to determine whether prenatal testing programmes are meeting their objectives.
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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 | 0.028 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.021 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".