Bibliographic record
Abstract
Medicine is incorporating genetic services into all avenues of health-care, ranging from the rarest to the most common diseases. Cognitive theories of decision-making still dominate professionals' understanding of patient decision-making about how to use genetic information and whether to have testing. I discovered a conceptual model of decision-making while carrying out a phenomenological-hermeneutic descriptive study of a convenience sample of 12 couples who were interviewed while deciding whether to undergo prenatal genetic testing. Thirty-two interviews were conducted with 12 men and 12 women separately. Interviews were transcribed verbatim and all data were analyzed using three levels of coding that were sorted into 30 categories and then abstracted into three emergent meta-themes that described men's and women's attempts to make sense and find meaning in how to best use prenatal genetic technology. Their descriptions of how they thought about, communicated, and coped with their decision were so detailed it was possible to discern nine different types of thinking they engaged in while deciding to accept or decline testing. They believed that decision-making is a process of working through your own personal style of thinking. This might include only one or any combination of the following types of thinking: analytical, ethical, moral, reflective, practical, hypothetical, judgmental, scary, and second sight, as described in detail by these 12 couples.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".